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Delivers structured briefings on hum...\n\nTags: VLA:1.0.3, ai:1.0.3, benchmarks:1.0.3, deployment:1.0.3, dexterous-manipulation:1.0.3, diffusion-policy:1.0.3, embodied-ai:1.0.3, foundation-models:1.0.3, funding:1.0.3, hardware:1.0.3, humanoid-robot:1.0.3, humanoid-robots:1.0.3, latest:1.0.5, news:1.0.3, research:1.0.3, robot:1.0.3, robot-learning:1.0.3, robotics:1.0.3, robotics-news:1.0.3, sim-to-real:1.0.3, world model:1.0.1, world-model:1.0.3\n\nVersion history:\n\nv1.0.5 | 2026-05-03T08:12:33.721Z | auto\n\nVersion 1.0.5\n\n- Updated references including taxonomy, workflow, GitHub repos, and source lists to align categories and SOPs.\n- Refined file interconnection descriptions for clarity and consistency across documentation.\n- Improved workflow details to synchronize steps in SKILL.md with updated reference files.\n- Ensured all trigger keywords, usage scenarios, and data-gathering phases reflect current supporting resources.\n- No logic or user-facing changes; focus is on documentation and internal process alignment.\n\nv1.0.4 | 2026-04-08T04:19:29.472Z | user\n\n- Adds reference file `github_repos.md` and integrates an optional GitHub module.\n- Now supports surfacing trending/open-source embodied AI GitHub repos (policies, simulation, datasets, benchmarks) by user request or trigger keyword.\n- Expands trigger keyword list to cover GitHub/open-source intents in English and Chinese.\n- Updates documentation and workflow: briefing can now optionally include a ranked open-source repo section for weekly/monthly or upon user request.\n- Reference file count increases from 5 to 6, with documentation on when to use the new GitHub reference.\n\nv1.0.3 | 2026-02-23T15:10:39.063Z | auto\n\n## embodied-ai-news 1.0.3 Changelog\n\n- Simplified and clarified the skill's description for accuracy and conciseness.\n- Updated workflow overview to a step-by-step execution guide, including explicit pre-search instructions on briefing type, time scope, and format.\n- Streamlined the “Reference Files” and their interconnection map for clearer relationships.\n- Added detailed, tool-based recipes for information gathering from web search, Tier 1 sources, arXiv, and company blogs.\n- Improved instructions for content extraction, deduplication, and output requirements.\n- Removed excessive procedural detail and restructured for better readability and user guidance.\n\nv1.0.2 | 2026-02-23T06:31:42.187Z | user\n\n- Reference file renamed: `news_source.md` replaced with `news_sources.md` for clarity and consistency.\n- Documentation and workflow instructions updated to refer to the correct filename (`news_sources.md`).\n- No functional or workflow changes. Update ensures all instructions and source references are accurate.\n\nv1.0.1 | 2026-02-23T06:18:21.314Z | user\n\n**Expanded trigger coverage and modular workflow for specialized briefings.**\n\n- Added support for monthly trend reports, policy/safety news, competitive analysis, and simulation/benchmark requests.\n- Expanded trigger keywords and sample queries in both English and Chinese; now recognizes more company names and research topics.\n- Introduced references to a new `workflow.md` for structured, variant-specific execution across daily, weekly, and monthly modes.\n- Updated reference file structure: now uses five documentation files (adds `workflow.md`, consolidates `news_source.md`).\n- Enhanced description of workflow variants, query strategies, and output template selection.\n- Improved modularity and cross-reference between reference files for more robust operation.\n\nv1.0.0 | 2026-02-23T05:44:12.254Z | user\n\nInitial release (v1.0.0)\n\nFeatures:\n- Multi-source news aggregation from 20+ robotics media outlets\n- Automated classification into 8 categories (Foundation Models, Hardware, Deployments, Funding, Policy, etc.)\n- Support for daily/weekly briefings with customizable depth\n- Bilingual support (English/Chinese)\n- arXiv paper tracking and analysis\n- Company-specific and technology-specific filtering\n- Safety & compliance standards (geopolitical neutrality, content filtering, source respect)\n- Real-time search integration with 15+ query recipes\n\nReference files included:\n- news_sources.md (curated source directory)\n- search_queries.md (query templates)\n- output_templates.md (6 format variants)\n- taxonomy.md (classification system & keyword dictionary)\n- workflow.md(detailed execution workflows)\n\nArchive index:\n\nArchive v1.0.5: 9 files, 54054 bytes\n\nFiles: references/github_repos.md (5864b), references/news_sources.md (21821b), references/output_templates.md (25475b), references/search_queries.md (18904b), references/taxonomy.md (30900b), references/workflow.md (17590b), skill-card.md (2608b), SKILL.md (24123b), _meta.json (135b)\n\nFile v1.0.5:SKILL.md\n\n---\nname: embodied-ai-news\ndescription: \"Aggregates publicly available Embodied AI and Robotics news from curated sources (robotics media, arXiv, company blogs). Delivers structured briefings on humanoid robots, foundation models, hardware, deployments, and funding with direct links to original articles. Optional module surfaces hot GitHub open-source repos relevant to embodied AI (policies, sim, data, benchmarks).\"\nhomepage: https://github.com/HeXavi8/skills\n---\n\n# Embodied AI News Briefing\n\n> Aggregates the latest Embodied AI & Robotics news from curated sources and delivers concise summaries with direct links. Covers the full stack: algorithms, hardware, simulation, deployment, funding, policy, and the China ecosystem.\n\n## When to Use This Skill\n\nActivate this skill when the user:\n\n- Asks for embodied AI news, robot news, or humanoid robot updates\n- Requests a daily/weekly/monthly robotics briefing\n- Mentions wanting to know what's happening in embodied AI / robotics\n- Asks about specific companies: Tesla Optimus, Figure, Unitree, AGIBOT, Boston Dynamics, etc.\n- Asks about specific technologies: VLA models, diffusion policy, sim-to-real, dexterous manipulation\n- Wants a summary of recent robotics research papers\n- Asks about robotics funding, deployments, or supply chain\n- Asks about simulation platforms, benchmarks, or datasets\n- Asks for **GitHub 热门仓库**、**具身智能开源项目**、**star 最多的机器人代码库**，或 wants a **repo leaderboard / open-source radar**\n- Asks about robotics policy, safety standards, or export controls\n- Requests a monthly trend report or competitive analysis\n- Says: \"给我今天的具身智能资讯\" (Give me today's embodied AI news)\n- Says: \"机器人行业有什么新动态\" (What's new in the robot industry)\n- Says: \"最近有什么人形机器人的消息\" (Any recent humanoid robot news)\n- Says: \"这个月的具身智能趋势报告\" (This month's embodied AI trend report)\n- Says: \"embodied AI updates\", \"robot learning news\", \"humanoid robot news\"\n\n### Trigger Keywords\n\n**English**: `embodied AI`, `humanoid robot`, `robot news`, `robotics update`, `robot learning`, `VLA model`, `diffusion policy`, `dexterous manipulation`, `sim-to-real`, `robot deployment`, `robotics funding`, `Figure AI`, `Tesla Optimus`, `Unitree`, `AGIBOT`, `Boston Dynamics`, `1X`, `Physical Intelligence`, `Skild AI`, `robot hand`, `quadruped robot`, `Isaac Sim`, `world model robot`, `robot benchmark`, `robot safety`, `robot regulation`, `monthly robot report`\n\n**Chinese**: `具身智能`, `人形机器人`, `机器人资讯`, `灵巧操作`, `仿真到真实`, `机器人部署`, `宇树`, `智元`, `优必选`, `银河通用`, `傅利叶`, `机器人融资`, `灵巧手`, `四足机器人`, `机器人大模型`, `机器人月报`, `机器人安全`, `机器人政策`, `GitHub 热门`, `开源仓库`, `机器人开源`\n\n---\n\n## Reference Files\n\nThis skill relies on **6** companion reference files. Always consult them during execution:\n\n```\n📁 references/\n├── 📰 news_sources.md        — WHERE to find information (tiered source list)\n├── 🔍 search_queries.md     — HOW to search (query templates & recipes)\n├── 📝 output_templates.md   — WHAT format to output (6+ template variants)\n├── 📊 taxonomy.md           — SHARED LANGUAGE (categories, keywords, company list)\n├── ⭐ github_repos.md       — GitHub hot repos module (discovery, ranking, output schema)\n└── 🧭 workflow.md           — WHEN and in what ORDER to execute (SOP for daily/weekly/monthly)\n```\n\n| File                  | When to Consult                                                                         |\n| --------------------- | --------------------------------------------------------------------------------------- |\n| `news_sources.md`     | Phase 1 — choosing which sites to fetch; selecting tier-appropriate sources             |\n| `search_queries.md`   | Phase 1 — building search queries; selecting recipe by briefing type                    |\n| `taxonomy.md`         | Phase 3 — classifying stories; Phase 1 — looking up company aliases & tech terms        |\n| `output_templates.md` | Phase 5 — rendering final output; selecting template by user request                    |\n| `github_repos.md`     | Phase 1 & 5 — when user wants GitHub 热门开源; weekly/monthly open-source momentum       |\n| `workflow.md`         | All Phases — orchestrating the end-to-end workflow; time budgeting; monthly maintenance |\n\n### File Interconnection Map\n\n```\n┌─────────────────┐      ┌────────────────────┐     ┌───────────────┐     ┌──────────────────┐\n│  search_queries │────▶ │  news_sources      │────▶│  Classify &   │────▶│ output_templates │\n│  (discover)     │      │  (browse & verify) │     │  Prioritize   │     │   (generate)     │\n└─────────────────┘      └────────────────────┘     └───────────────┘     └──────────────────┘\n                                    ▲                        ▲\n                                    │                        │\n                                    └────── taxonomy.md ─────┘\n                                         (shared vocabulary)\n\nOptional GitHub module:\n  search_queries (Recipe F) ──▶ github_repos.md ──▶ output_templates (⭐ GitHub section)\n```\n\n---\n\n## Execution Workflow\n\n### Phase 0: Determine Briefing Type & Time Scope\n\n**Before any tool calls**, ask the user (if not already clear):\n\n1. **Briefing Type**: Daily / Weekly / Monthly / Custom Topic?\n2. **Time Scope**: Last 24 hours / Last 7 days / Last 30 days / Custom date range?\n3. **Output Format**: Standard / Brief / Thread / Markdown Report / Presentation / Custom?\n4. **Focus Area** (optional): All categories / Specific category (e.g., only hardware, only China ecosystem)?\n5. **GitHub 开源模块** (optional): Include **hot embodied-AI repos** section? (Default: **Yes** for weekly/monthly if user asked for “完整/含开源”; **No** for daily unless requested.)\n\n**Default if user doesn't specify**:\n\n- Type: Daily\n- Scope: Last 24 hours\n- Format: Standard\n- Focus: All categories\n- GitHub module: **Off** for daily; **Off** for weekly/monthly unless user implies open-source / GitHub / 技术栈雷达\n\n**Map to workflow.md**:\n\n- Daily → `workflow.md` Section \"Daily Workflow\"\n- Weekly → `workflow.md` Section \"Weekly Workflow\"\n- Monthly → `workflow.md` Section \"Monthly Workflow\"\n\n---\n\n### Phase 1: Information Gathering\n\nConsult `workflow.md` for the appropriate recipe, then execute the corresponding steps from `search_queries.md` and `news_sources.md`.\n\n#### Step 1.1: Execute Search Queries\n\n**Tool**: `WebSearch` (or equivalent web search tool)\n\n**Source**: `search_queries.md` → Select the appropriate recipe:\n\n- Daily Briefing → Recipe A (5 queries)\n- Weekly Roundup → Recipe B (8 queries)\n- Monthly Deep Dive → Recipe C (12 queries)\n- Custom Topic → Recipe D + user-specified filters\n\n**Parameters**:\n\n- `return_format`: markdown\n- `with_images_summary`: false\n- `timeout`: 20 seconds per source\n- Fetch only from publicly accessible sources listed in `news_sources.md`\n\n**Output**: A list of 20–50 URLs with headlines and snippets.\n\n---\n\n#### Step 1.2: Fetch Tier 1 Sources Directly\n\n**Tool**: `mcp__web_reader__webReader`\n\n**Source**: `news_sources.md` → Tier 1 section\n\nDirectly fetch the homepage or RSS feed of:\n\n- The Robot Report\n- IEEE Spectrum — Robotics\n- TechCrunch — Robotics\n- Robotics Business Review\n- (Add others based on briefing type)\n\n**Parameters**:\n\n- `url`: [homepage URL from news_sources.md]\n- `return_format`: markdown\n- `with_images_summary`: false\n- Process only URLs from verified sources in `news_sources.md`\n\n**Output**: Recent headlines (last 24h / 7d / 30d based on scope).\n\n---\n\n#### Step 1.3: Fetch arXiv Papers\n\n**Tool**: `mcp__arxiv__readURL` (if available) or `WebSearch` with arXiv-specific queries\n\n**Source**: `search_queries.md` → Section \"6. Academic Research (arXiv)\"\n\nExecute 2–3 arXiv queries:\n\n```\ncat:cs.RO AND (\"embodied AI\" OR \"robot learning\" OR \"VLA\") submittedDate:[today - 7d TO today]\n```\n\n**Output**: 5–10 recent papers with abstracts.\n\n---\n\n#### Step 1.4: Fetch Company Blogs & Official Announcements\n\n**Tool**: `mcp__web_reader__webReader`\n\n**Source**: `news_sources.md` → Tier 2 (Company Blogs) + Tier 4 (China Ecosystem)\n\nFetch from:\n\n- Figure AI Blog\n- Physical Intelligence Blog\n- Tesla AI Blog\n- Unitree Blog (Chinese + English)\n- AGIBOT WeChat Official Account (if accessible)\n- (Add others based on focus area)\n\n**Fetch constraints**:\n\n- Only process URLs from search results and sources listed in `news_sources.md`\n- Skip content requiring authentication\n- Timeout: 15 seconds per URL\n\n**Output**: Recent announcements (last 7d / 30d based on scope).\n\n---\n\n#### Step 1.5: GitHub — Hot Embodied AI Repos (Optional)\n\n**When**: User requested the GitHub module (Phase 0), or weekly/monthly briefing explicitly includes open-source radar.\n\n**Tools**: `WebSearch`, `WebFetch` (or equivalent) — **no** GitHub token required; use public pages only.\n\n**Source**: `github_repos.md` (full procedure) + `search_queries.md` → **Section 10.5** + **Recipe F**\n\n**Procedure** (summary):\n\n1. Run **Recipe F** queries; collect **12–20** candidates.\n2. Filter with **`github_repos.md` → Relevance Filter**; verify each shortlisted repo URL.\n3. Rank per **`github_repos.md` → Rank (“热门” definition)**; output **5–8** repos.\n4. Do **not** invent star counts; use verified values or “see repo page”.\n\n**Output**: Structured rows ready for **`output_templates.md` → GitHub 热门开源** section; deduplicate against stories already covered in Foundation Models / Simulation sections.\n\n---\n\n### Phase 2: Content Extraction & Deduplication\n\nFor each fetched URL:\n\n1. **Extract**:\n   - Headline\n   - Publication date\n   - Source name\n   - Summary (first 2–3 paragraphs or abstract)\n   - Key entities: companies, models, hardware platforms (use `taxonomy.md` for reference)\n\n2. **Deduplicate**:\n   - If multiple sources cover the same story, keep the one with the most detail\n   - Merge information if they provide complementary details\n\n3. **Discard**:\n   - Stories older than the time scope\n   - Irrelevant content (use `search_queries.md` Section 1.4 \"Noise Exclusion Filter\")\n   - Duplicate announcements\n\n**Output**: A deduplicated list of 15–30 stories with extracted metadata.\n\n---\n\n### Phase 3: Classification & Prioritization\n\nConsult `taxonomy.md` to classify each story.\n\n#### Step 3.1: Assign Primary Category\n\nUse `taxonomy.md` → Section \"1. News Category Taxonomy\"\n\nAssign each story to **exactly one** primary category:\n\n- 🔥 Major Announcements\n- 🧠 Foundation Models & Algorithms\n- 🦾 Hardware & Platforms\n- 🌐 Simulation & Infrastructure\n- 🏭 Deployments & Commercial\n- 💰 Funding, M&A & Business\n- 🌍 Policy, Safety & Ethics\n- 🇨🇳 China Ecosystem\n\n**Rules** (from `taxonomy.md` → \"Category Assignment Rules\"):\n\n- **Major Announcements**: Only for top-impact stories (new paradigm, >$500M funding, first-ever deployment milestone)\n- **China Ecosystem**: Use when the story's primary significance is about the Chinese market/ecosystem\n- **Cross-cutting stories**: Assign primary + up to 2 secondary tags\n\n---\n\n#### Step 3.2: Assign Priority Level\n\nUse `taxonomy.md` → Section \"3. Priority Scoring System\"\n\nCalculate priority score (0–100) based on:\n\n- **Impact** (0–40 points): Paradigm shift / Major milestone / Incremental improvement\n- **Timeliness** (0–20 points): Breaking news / Recent (1–3 days) / Older\n- **Source Authority** (0–20 points): Tier 1 / Tier 2 / Tier 3\n- **Relevance** (0–20 points): Core embodied AI / Adjacent / Tangential\n\n**Priority Levels**:\n\n- **P0 (90–100)**: Must-read, above-the-fold\n- **P1 (70–89)**: Important, include in main body\n- **P2 (50–69)**: Notable, include if space allows\n- **P3 (<50)**: Optional, move to \"Other News\" section or omit\n\n---\n\n#### Step 3.3: Sort Stories\n\nWithin each category, sort by:\n\n1. Priority score (descending)\n2. Publication date (most recent first)\n\n---\n\n### Phase 4: Content Synthesis\n\nFor each story, generate:\n\n1. **One-sentence summary**: Capture the core news in <20 words\n2. **Key points** (2–4 bullet points): Extract the most important details\n3. **Metadata fields** (based on category):\n   - For **Foundation Models**: Model Type, Embodiment, Open Source, Impact\n   - For **Hardware**: Hardware Type, Company, Specs, Impact\n   - For **Deployments**: Deployment Scale, Industry Vertical, Performance Metrics, Impact\n   - For **Funding**: Amount, Lead Investor, Valuation, Use of Funds\n   - (See `output_templates.md` for full metadata schema per category)\n\n4. **Impact statement**: Why this matters for the embodied AI field (1–2 sentences)\n\n**Tone & Style**:\n\n- **Objective**: Present facts without hype or editorial opinion\n- **Concise**: Favor clarity over completeness\n- **Technical**: Use domain-specific terminology from `taxonomy.md`\n- **Neutral**: Treat all companies, countries, and technologies equally\n\n---\n\n### Phase 5: Output Generation\n\nConsult `output_templates.md` to select the appropriate template.\n\n#### Step 5.1: Select Template\n\nBased on user request (from Phase 0):\n\n| User Request          | Template to Use            |\n| --------------------- | -------------------------- |\n| \"Daily briefing\"      | Standard Format            |\n| \"Quick summary\"       | Brief Format               |\n| \"Twitter thread\"      | Thread Format              |\n| \"Markdown report\"     | Markdown Report Format     |\n| \"Presentation slides\" | Presentation Format        |\n| \"Custom\"              | Adapt from Standard Format |\n\n---\n\n#### Step 5.2: Render Output\n\nFill in the selected template with:\n\n- **Header**: Date, source count, time scope\n- **Category sections**: Ordered by priority (🔥 Major Announcements first)\n- **Story blocks**: Headline, summary, key points, metadata, source link\n- **GitHub 热门开源** (if Step 1.5 ran): Place **before** Key Takeaways / Daily Pulse per `output_templates.md`\n- **Footer**: Methodology note, source attribution\n\n**Quality checks**:\n\n- All links are valid and correctly formatted\n- All metadata fields are filled (use \"N/A\" if not applicable)\n- No duplicate stories\n- Stories are sorted by priority within each category\n- Total output length is appropriate for briefing type:\n  - Daily: 1,500–2,500 words\n  - Weekly: 3,000–5,000 words\n  - Monthly: 5,000–10,000 words\n\n---\n\n#### Step 5.3: Add Contextual Notes (Optional)\n\nIf the user requested analysis or trends, append:\n\n- **Trend Spotlight**: 2–3 emerging patterns observed this period\n- **Company Momentum**: Which companies/labs are most active\n- **Technology Shifts**: Notable changes in technical approaches\n- **Geographic Insights**: Regional differences (e.g., US vs China ecosystem)\n\nUse `taxonomy.md` → Section \"5. Trend Analysis Framework\" for guidance.\n\n---\n\n### Phase 6: Delivery & Follow-up\n\n1. **Deliver the briefing** in the selected format\n2. **Offer follow-up options**:\n   - \"Would you like me to deep-dive into any specific story?\"\n   - \"Should I track these companies/topics for your next briefing?\"\n   - \"Would you like a comparison with last week/month's trends?\"\n\n---\n\n## Special Workflows\n\n### Custom Topic Deep-Dive\n\nIf user asks about a specific topic (e.g., \"What's new with dexterous hands?\"):\n\n1. **Consult** `taxonomy.md` → Section \"2. Technology & Product Taxonomy\" → Find relevant subcategories\n2. **Build custom queries** using `search_queries.md` → Recipe D (Custom Topic)\n3. **Fetch** from all tiers in `news_sources.md` that cover this topic\n4. **Output** using the \"Deep-Dive Format\" from `output_templates.md`\n\n---\n\n### Company-Specific Briefing\n\nIf user asks about a specific company (e.g., \"What's Figure AI been up to?\"):\n\n1. **Consult** `taxonomy.md` → Section \"4. Company & Organization Directory\" → Find company profile\n2. **Build queries** targeting:\n   - Company blog\n   - News mentions\n   - arXiv papers by company researchers\n   - Funding announcements\n3. **Output** using the \"Company Spotlight Format\" from `output_templates.md`\n\n---\n\n### China Ecosystem Focus\n\nIf user asks specifically about China (e.g., \"中国人形机器人有什么进展?\"):\n\n1. **Prioritize** `news_sources.md` → Tier 4 (China Ecosystem)\n2. **Use** `search_queries.md` → Section \"8. China Ecosystem\"\n3. **Consult** `taxonomy.md` → Section \"4.3 China Ecosystem Companies\"\n4. **Output** in Chinese or bilingual format (ask user preference)\n\n---\n\n### GitHub Open-Source Radar Only\n\nIf the user **only** wants a **GitHub 热门仓库** snapshot (no full news briefing):\n\n1. **Skip** or minimize Steps 1.1–1.4; run **`github_repos.md`** procedure end-to-end with **Recipe F**\n2. **Output** using **`output_templates.md`** → **⭐ GitHub** section (Standard or Brief) plus a short **methodology** footnote\n3. **Language**: Match user language; keep repo names in original spelling\n\n---\n\n## Operational Guidelines\n\n### Operating Scope\n\nThis skill operates in **read-only mode**:\n\n- Fetches content from public sources listed in reference files\n- Synthesizes and presents information to the user\n- Does not modify, post, or interact with external systems\n- Does not perform actions on behalf of the user unless explicitly requested (e.g., \"add this to my calendar\")\n\n#### Self-Modification Guard\n\n- The agent must **never** silently modify the skill's own reference files (`news_sources.md`, `search_queries.md`, `github_repos.md`, `output_templates.md`, `workflow.md`, `taxonomy.md`)\n- All proposed changes to these files must be presented as a **Maintenance Proposal** with a diff, and only applied after explicit user approval — see `workflow.md` → Part B\n- User-facing reports and maintenance edits must remain separate; briefings never trigger file modifications\n\n### Information Freshness\n\n- **Daily briefing**: Prioritize stories from the last 24 hours\n- **Weekly briefing**: Include stories from the last 7 days, but highlight the most recent\n- **Monthly briefing**: Cover the full 30 days, but organize by week or theme\n\n### Source Diversity\n\nAim for a balanced mix:\n\n- 40% from Tier 1 (core industry media)\n- 30% from Tier 2 (company blogs & official sources)\n- 20% from Tier 3 (academic & research)\n- 10% from Tier 4 (China ecosystem, if relevant)\n\n### Quality over Quantity\n\n- Better to have 15 high-quality, well-summarized stories than 50 shallow headlines\n- If a story lacks detail or verification, mark it as \"Unconfirmed\" or omit it\n\n### Handling Uncertainty\n\n- If a story's details are unclear, state: \"Details are limited; awaiting official confirmation\"\n- If sources conflict, present both versions: \"Source A reports X, while Source B reports Y\"\n- Never fabricate details to fill gaps\n\n### Language Handling\n\n- If user asks in Chinese, output in Chinese (but keep company/model names in English)\n- If user asks in English, output in English\n- For bilingual users, offer: \"Would you like this in English, Chinese, or bilingual?\"\n\n---\n\n## Error Handling\n\n### If a source is unreachable:\n\n- Skip it and note in the footer: \"Note: [Source Name] was unavailable at the time of this briefing\"\n\n### If search returns no results:\n\n- Broaden the query or try alternative keywords from `taxonomy.md`\n- If still no results, inform the user: \"No recent news found for [topic] in the specified time range\"\n\n### If classification is ambiguous:\n\n- Default to the most specific applicable category\n- Add a secondary tag if the story spans multiple domains\n\n### If output exceeds length limits:\n\n- Prioritize P0 and P1 stories\n- Move P2 and P3 stories to a \"Quick Hits\" section with one-line summaries\n- Offer to generate a separate deep-dive on omitted topics\n\n---\n\n## Maintenance & Updates\n\n### Monthly (consult `workflow.md` → \"Monthly Workflow\"):\n\n- Review `taxonomy.md` for new companies, models, or terminology\n- **Propose** updates to `news_sources.md` if new authoritative sources emerge\n- **Propose** refinements to `search_queries.md` based on what queries yielded the best results\n- **Propose** updates to `github_repos.md` anchor list and Recipe F queries\n\n**⚠️ All reference file changes require explicit user approval.** The agent generates a Maintenance Proposal (see `workflow.md` → Part B) and presents it as a diff. Do not write to any reference file without user confirmation.\n\n### Quarterly:\n\n- Audit the priority scoring system — are P0 stories truly the most impactful?\n- Review output templates — do they match user preferences?\n\n---\n\n## Example Invocations\n\n### Example 1: Daily Briefing\n\n**User**: \"Give me today's embodied AI news\"\n\n**Agent**:\n\n1. Determines: Daily briefing, last 24h, Standard format, All categories\n2. Executes Recipe A from `search_queries.md` (5 queries)\n3. Fetches Tier 1 sources from `news_sources.md`\n4. Classifies using `taxonomy.md`\n5. Outputs using Standard Format from `output_templates.md`\n\n---\n\n### Example 2: Weekly Roundup\n\n**User**: \"What happened in robotics this week?\"\n\n**Agent**:\n\n1. Determines: Weekly briefing, last 7 days, Standard format, All categories\n2. Executes Recipe B from `search_queries.md` (8 queries)\n3. Fetches Tier 1 + Tier 2 sources\n4. Prioritizes P0 and P1 stories\n5. Outputs using Standard Format with \"Trend Spotlight\" section\n\n---\n\n### Example 3: Custom Topic\n\n**User**: \"What's new with VLA models?\"\n\n**Agent**:\n\n1. Determines: Custom topic, last 7 days, Deep-Dive format\n2. Consults `taxonomy.md` → \"Vision-Language-Action (VLA) Models\"\n3. Builds custom queries from `search_queries.md` Section 2.1\n4. Fetches from Tier 1 + Tier 3 (arXiv)\n5. Outputs using Deep-Dive Format\n\n---\n\n### Example 4: Company Spotlight\n\n**User**: \"What's Unitree been up to?\"\n\n**Agent**:\n\n1. Determines: Company-specific, last 30 days, Company Spotlight format\n2. Consults `taxonomy.md` → Company profile for Unitree\n3. Fetches Unitree blog + news mentions + arXiv papers\n4. Outputs using Company Spotlight Format from `output_templates.md`\n\n---\n\n### Example 5: China Ecosystem\n\n**User**: \"中国人形机器人有什么进展?\"\n\n**Agent**:\n\n1. Determines: China focus, last 7 days, Standard format, Chinese output\n2. Prioritizes `news_sources.md` Tier 4 sources\n3. Uses `search_queries.md` Section 8 (China Ecosystem)\n4. Outputs in Chinese using Standard Format\n\n---\n\n### Example 6: GitHub Hot Repos Add-on\n\n**User**: \"今天的具身智能资讯里加上 GitHub 最热门的相关开源仓库\"\n\n**Agent**:\n\n1. Enables GitHub module for this run; keeps daily scope if user asked “今天”\n2. Executes **Recipe F** from `search_queries.md` and follows **`github_repos.md`** (verify URLs, no fake stars)\n3. Inserts **`## ⭐ GitHub 热门开源（具身智能相关）`** from `output_templates.md` **before** Key Takeaways\n4. Shortlists **5–8** repos with category tags and canonical `https://github.com/owner/repo` links\n\n---\n\n## Summary\n\nThis skill orchestrates a multi-phase workflow:\n\n1. **Determine** briefing type & scope (including optional GitHub module)\n2. **Gather** information from curated sources using structured queries\n3. **Classify** stories using a shared taxonomy\n4. **Prioritize** based on impact, timeliness, and relevance\n5. **Synthesize** concise summaries with metadata\n6. **Output** in the user's preferred format (with optional **GitHub 热门开源** section)\n\n**Key success factors**:\n\n- Always consult the **6** reference files at the appropriate workflow stage\n- Maintain objectivity and source attribution\n- Prioritize quality and relevance over quantity\n- Adapt to user preferences (language, format, focus area)\n\nFile v1.0.5:_meta.json\n\n{\n  \"ownerId\": \"kn7arpc65p9wdnhbw70435rrf181jvtk\",\n  \"slug\": \"embodied-ai-news\",\n  \"version\": \"1.0.5\",\n  \"publishedAt\": 1777795953721\n}\n\nFile v1.0.5:references/github_repos.md\n\n# ⭐ GitHub — Embodied AI Open Source Hot Repos\n\nCompanion reference for the **GitHub 热门开源仓库** module: how to discover, rank, and present repositories that are most relevant to embodied AI / robot learning (not generic industrial automation or unrelated “robot” tooling).\n\n---\n\n## When to Use This File\n\nConsult during **Phase 1** (gathering) and **Phase 5** (output) when:\n\n- The user asks for **GitHub 热门**、**开源仓库**、**star 最多的机器人项目**，或 explicitly wants a **repo leaderboard**\n- The briefing type is **Weekly** or **Monthly** and the user wants **open-source momentum** included\n- You are filling the **`## ⭐ GitHub 热门开源（具身智能相关）`** section in `output_templates.md`\n\n**Default**: Do **not** add this section to a **Daily** briefing unless the user asked for it or for “full stack / 含开源”.\n\n---\n\n## Data Sources & Tools\n\n| Source | Tool | Notes |\n|--------|------|--------|\n| GitHub repository search (sorted) | `WebSearch` + `WebFetch` | Prefer official `github.com` URLs; verify repo still exists |\n| GitHub Topics | `WebFetch` | e.g. topic pages for `robotics`, `reinforcement-learning`, `sim2real` |\n| Curated lists / “awesome-*” | `WebSearch` | Use to cross-check names; still verify primary repo URL on GitHub |\n\n**Do not** fabricate star counts or “#1 trending” claims. Use numbers **only** if visible on the fetched GitHub page or in search snippets at collection time; otherwise write **“stars: see repo page”** or omit the column.\n\n---\n\n## Relevance Filter (Must Pass)\n\nA repo **qualifies** for this module if it clearly supports **one or more** of:\n\n- **Policies / models**: VLA, diffusion / flow policies, imitation / offline RL, world models for control\n- **Data & teleop**: datasets, teleoperation stacks, human demo pipelines\n- **Simulation → real**: Isaac / MuJoCo / Habitat-class stacks, domain randomization, sim benchmarks\n- **Whole-body / manipulation**: humanoid / quadruped / arm stacks where **learning** or **ML policy** is central\n- **Embodied foundation models**: GR00T-class, generalist robot models, cross-embodiment training code\n\n**Deprioritize or exclude** unless the user asks broadly:\n\n- Pure motion planning / classic control with no learning angle\n- Arduino / ROS tutorial repos with no embodied-AI focus\n- Unmanned vehicles / autopilot-only (unless explicitly in scope)\n- Empty forks, archived with no replacement, or name-squatting\n\n---\n\n## Discovery Procedure (Executable)\n\n### Step G1 — Run discovery queries\n\nUse `search_queries.md` → **Section 10.5** and **Recipe F** (`WebSearch`, `return_format`: markdown).\n\nMinimum: **3 queries** from Recipe F (rotate which sub-queries you use if a run returns noise).\n\n### Step G2 — Collect candidates\n\nTarget **12–20** candidate repos, then **shortlist 5–8** for the briefing.\n\n### Step G3 — Rank (“热门” definition)\n\nApply in order (break ties by recency of meaningful commits / releases if visible):\n\n1. **Ecosystem impact**: widely cited stacks (sim, benchmark, policy zoo), de-facto standard tooling\n2. **Recent activity**: releases, tags, or default-branch commits in the last **30 days** (if checkable)\n3. **Stars**: higher is a weak proxy for popularity — use only as **one** signal\n4. **Narrow spikes**: very new repos with explosive stars — label **“Emerging (high velocity)”** if you have evidence\n\n### Step G4 — Verify\n\nFor each shortlisted repo:\n\n- Open `https://github.com/{owner}/{repo}` (via `WebFetch` or browser)\n- Confirm **description**, **default branch**, **not archived** (or explain if archived but still the canonical fork)\n- Copy the **canonical** URL (no deep `/tree/` unless linking to a specific release tag the user needs)\n\n### Step G5 — De-duplicate vs news body\n\nIf a repo is **already** the main subject of a story in **Foundation Models & Algorithms** or **Simulation & Infrastructure**, you may **merge**: one line in the GitHub table + “See story above” instead of repeating full paragraphs.\n\n---\n\n## Output Schema (per repo)\n\nUse in markdown table or bullet blocks (see `output_templates.md`):\n\n| Field | Required | Example |\n|-------|----------|---------|\n| **Repository** | Yes | `org/name` as link |\n| **One-line role** | Yes | “VLA training & eval for …” |\n| **Category tag** | Yes | From list below |\n| **Language** | If visible | Python / C++ / … |\n| **Stars** | If verified | `12.4k` or “see repo” |\n| **Activity note** | Optional | “Release x.y last week” |\n\n**Category tags** (pick one primary):\n\n`Policy / VLA` · `Sim & Sim2Real` · `Data / Teleop` · `Whole-body / Locomotion` · `Manipulation` · `Benchmark / Eval` · `Hardware / Middleware`\n\n---\n\n## Anchor List (Sanity Check, Not Exhaustive)\n\nUse to avoid missing obvious high-signal projects when search is noisy; **always** re-validate on GitHub:\n\n- NVIDIA: `IsaacLab`, `GR00T` (and related)\n- Google DeepMind: `mujoco`, `dm_control`\n- Meta FAIR: `habitat-lab`, `PyTorch3D` (when used for embodied / 3D policy research)\n- Open Robotics middleware: `ros2` (include only if briefing scope covers **ML-on-ROS** embodied stacks)\n- Community: `lerobot`, `robosuite`, `mani_skill`, `ORBIT`, `IsaacGymEnvs` (legacy but still referenced)\n\nIf an anchor is **archived** or superseded, prefer the **successor** named in the repo README.\n\n---\n\n## Footnote for Briefings\n\nAppend to the GitHub section when star counts or rankings are approximate:\n\n> **Note**: Rankings reflect search-time signals (stars, activity, ecosystem role), not a guaranteed global order. Prefer following the linked repo for authoritative metrics.\n\n---\n\n> **Last Updated**: April 2026  \n> **Maintainer cue**: Refresh anchor names and topic URLs quarterly via the Maintenance Proposal workflow (`workflow.md` Part B). Do not modify this file without user approval.\n\nFile v1.0.5:references/news_sources.md\n\n# 🤖 Embodied AI — Comprehensive News Source Guide\n\nA curated directory of the most authoritative information sources in the Embodied AI space, covering hardware R&D, foundation models, algorithms, commercial deployment, policy, investment, and academic research.\n\n---\n\n## Tier 1: Core Industry Media (Daily Must-Reads)\n\nThese outlets provide dedicated, deep-dive coverage of the intersection of robotics and AI.\n\n### 1. The Robot Report\n- **URL**: [https://www.therobotreport.com/](https://www.therobotreport.com/)\n- **Frequency**: Daily\n- **Focus**: Industrial robots, humanoid robots, commercial deployments, industry earnings\n- **Best For**: Tracking business moves and technology adoption across the robotics industry\n- **Key Strength**: The \"Wall Street Journal\" of robotics — deep commercial analysis and trend reporting.\n\n### 2. IEEE Spectrum — Robotics\n- **URL**: [https://spectrum.ieee.org/topic/robotics/](https://spectrum.ieee.org/topic/robotics/)\n- **Frequency**: Daily\n- **Focus**: Cutting-edge robotics tech, lab prototypes, sensors & actuators\n- **Best For**: Understanding the latest engineering breakthroughs and hardcore technical details\n- **Key Strength**: Operated by IEEE; extremely high technical authority with exclusive interviews (notably Evan Ackerman's coverage).\n\n### 3. TechCrunch — Robotics\n- **URL**: [https://techcrunch.com/category/robotics/](https://techcrunch.com/category/robotics/)\n- **Frequency**: Daily\n- **Focus**: Startup funding rounds, product launches, VC landscape in robotics\n- **Best For**: Tracking investment flows and emerging startups in embodied AI\n- **Key Strength**: First-mover on breaking funding news; strong Silicon Valley network.\n\n### 4. Robotics Business Review (RBR)\n- **URL**: [https://www.roboticsbusinessreview.com/](https://www.roboticsbusinessreview.com/)\n- **Frequency**: Daily\n- **Focus**: Enterprise robotics, ROI analysis, market sizing, automation strategy\n- **Best For**: Business leaders evaluating robotics adoption\n- **Key Strength**: Focused on the business case for robotics, not just the tech.\n\n### 5. RoboticsTomorrow\n- **URL**: [https://www.roboticstomorrow.com/](https://www.roboticstomorrow.com/)\n- **Frequency**: Daily\n- **Focus**: Industry predictions, automation trends, embodied AI outlook\n- **Best For**: Broad industry pulse and expert opinion pieces\n- **Key Strength**: Aggregates expert perspectives from across the robotics ecosystem.\n\n### 6. Interesting Engineering — Robotics\n- **URL**: [https://interestingengineering.com/innovation/robotics](https://interestingengineering.com/innovation/robotics)\n- **Frequency**: Daily\n- **Focus**: Humanoid robots, viral robot demos, hardware innovation\n- **Best For**: Accessible, visual-first reporting on the latest robot developments\n- **Key Strength**: Excellent at capturing viral moments and translating them into context-rich stories.\n\n---\n\n## Tier 2: Top Company Official Blogs (Weekly Focus)\n\nBreakthroughs in embodied AI are often driven by leading companies. Official blogs are the only source for first-hand, accurate information.\n\n### 1. Tesla AI & Robotics\n- **URL**: [https://x.com/Tesla_AI](https://x.com/Tesla_AI) / [https://www.tesla.com/AI](https://www.tesla.com/AI)\n- **Frequency**: Sporadic (around major releases)\n- **Focus**: Optimus humanoid robot, FSD algorithm transfer, mass production\n- **Key Content**: Optimus iteration videos, end-to-end neural network training progress.\n\n### 2. NVIDIA Isaac / Robotics Blog\n- **URL**: [https://developer.nvidia.com/blog/category/robotics/](https://developer.nvidia.com/blog/category/robotics/)\n- **Frequency**: Weekly\n- **Focus**: Isaac Sim, robot foundation models, edge computing hardware\n- **Key Content**: Simulation environment updates, GR00T / Isaac Lab progress, multimodal learning frameworks.\n\n### 3. Google DeepMind — Robotics\n- **URL**: [https://deepmind.google/discover/blog/](https://deepmind.google/discover/blog/)\n- **Frequency**: Monthly (deep updates)\n- **Focus**: RT-2, AutoRT, Vision-Language-Action (VLA) models\n- **Key Content**: Major breakthroughs in robot foundation models.\n\n### 4. Figure AI\n- **URL**: [https://www.figure.ai/news](https://www.figure.ai/news)\n- **Frequency**: Sporadic\n- **Focus**: Humanoid robot + LLM integration, BMW factory deployment\n- **Key Content**: Real-world industrial deployment case studies.\n\n### 5. Boston Dynamics Blog\n- **URL**: [https://bostondynamics.com/blog/](https://bostondynamics.com/blog/)\n- **Frequency**: Monthly\n- **Focus**: Electric Atlas, Spot, Stretch; locomotion & manipulation research\n- **Key Content**: New platform capabilities, customer deployment stories, research papers.\n\n### 6. 1X Technologies\n- **URL**: [https://www.1x.tech/discover](https://www.1x.tech/discover)\n- **Frequency**: Sporadic\n- **Focus**: NEO humanoid, learned behaviors, embodied AI safety\n- **Key Content**: Progress on general-purpose humanoid robots for homes and workplaces.\n\n### 7. Agility Robotics\n- **URL**: [https://agilityrobotics.com/news](https://agilityrobotics.com/news)\n- **Frequency**: Monthly\n- **Focus**: Digit humanoid, warehouse logistics, RoboFab manufacturing\n- **Key Content**: Commercial deployment milestones, Digit platform updates.\n\n### 8. Sanctuary AI\n- **URL**: [https://www.sanctuary.ai/blog](https://www.sanctuary.ai/blog)\n- **Frequency**: Monthly\n- **Focus**: Phoenix humanoid, Carbon AI control system, dexterous manipulation\n- **Key Content**: Reinforcement learning for hydraulic hands, general-purpose robot intelligence.\n\n### 9. Unitree Robotics\n- **URL**: [https://www.unitree.com/](https://www.unitree.com/) / [https://x.com/UnitreeRobotics](https://x.com/UnitreeRobotics)\n- **Frequency**: Sporadic\n- **Focus**: G1/H1 humanoid, Go2 quadruped, affordable robotics hardware\n- **Key Content**: New product launches, viral demo videos, open-source SDK updates.\n\n### 10. Skild AI\n- **URL**: [https://www.skild.ai/](https://www.skild.ai/)\n- **Frequency**: Sporadic\n- **Focus**: General-purpose robot foundation model, scalable robot brain\n- **Key Content**: Foundation model architecture, cross-embodiment transfer learning.\n\n### 11. Physical Intelligence (π)\n- **URL**: [https://www.physicalintelligence.company/blog](https://www.physicalintelligence.company/blog)\n- **Frequency**: Sporadic\n- **Focus**: Foundation models for physical interaction, generalist robot policies\n- **Key Content**: π0 model releases, cross-task generalization research.\n\n### 12. Covariant (now part of Amazon Robotics)\n- **URL**: [https://covariant.ai/](https://covariant.ai/)\n- **Frequency**: Sporadic\n- **Focus**: AI-powered robotic picking, warehouse automation, RFM (Robotics Foundation Model)\n- **Key Content**: Large-scale deployment data, model performance benchmarks.\n\n### 13. Toyota Research Institute (TRI)\n- **URL**: [https://www.tri.global/news](https://www.tri.global/news)\n- **Frequency**: Monthly\n- **Focus**: Diffusion policy, dexterous manipulation, household robotics\n- **Key Content**: Novel learning approaches for robot dexterity, Large Behavior Models (LBMs).\n\n### 14. Meta AI — Robotics\n- **URL**: [https://ai.meta.com/blog/](https://ai.meta.com/blog/) (filter for robotics)\n- **Frequency**: Sporadic\n- **Focus**: Embodied AI benchmarks (Habitat), egocentric perception, open-source models\n- **Key Content**: Habitat simulator updates, Ego4D dataset, open-source embodied AI tools.\n\n---\n\n## Tier 3: Academic & Research Sources (Weekly Review)\n\nEmbodied AI is a highly research-driven field. Tracking top conferences and preprints is key to staying ahead.\n\n### 1. arXiv — Robotics (cs.RO)\n- **URL**: [https://arxiv.org/list/cs.RO/recent](https://arxiv.org/list/cs.RO/recent)\n- **Frequency**: Daily\n- **Focus**: Motion planning, embodied perception, reinforcement learning\n- **Key Strength**: Access to cutting-edge algorithms before formal publication.\n\n### 2. arXiv — Computer Vision (cs.CV) & Machine Learning (cs.LG)\n- **URL**: [https://arxiv.org/list/cs.CV/recent](https://arxiv.org/list/cs.CV/recent) / [https://arxiv.org/list/cs.LG/recent](https://arxiv.org/list/cs.LG/recent)\n- **Frequency**: Daily\n- **Focus**: VLA models, world models, sim-to-real transfer, 3D scene understanding\n- **Key Strength**: Many embodied AI foundation model papers appear here first.\n\n### 3. CoRL (Conference on Robot Learning)\n- **URL**: [https://www.corl.org/](https://www.corl.org/)\n- **Frequency**: Annual (+ post-conference proceedings)\n- **Focus**: Robot learning, imitation learning, end-to-end control\n- **Best For**: Deep academic foundations of embodied AI.\n\n### 4. RSS (Robotics: Science and Systems)\n- **URL**: [https://roboticsconference.org/](https://roboticsconference.org/)\n- **Frequency**: Annual\n- **Focus**: Core robotics algorithms, planning, perception, manipulation\n- **Best For**: High-quality, peer-reviewed robotics research.\n\n### 5. ICRA (IEEE International Conference on Robotics and Automation)\n- **URL**: [https://www.ieee-ras.org/conferences-workshops/fully-sponsored/icra](https://www.ieee-ras.org/conferences-workshops/fully-sponsored/icra)\n- **Frequency**: Annual\n- **Focus**: The largest robotics conference — covers all subfields\n- **Best For**: Comprehensive overview of the entire robotics research landscape.\n\n### 6. IROS (IEEE/RSJ International Conference on Intelligent Robots and Systems)\n- **URL**: [https://www.iros.org/](https://www.iros.org/)\n- **Frequency**: Annual\n- **Focus**: Intelligent systems, human-robot interaction, field robotics\n- **Best For**: Applied robotics research and systems integration.\n\n### 7. NeurIPS / ICML / ICLR — Robot Learning Workshops\n- **URL**: [https://neurips.cc/](https://neurips.cc/) / [https://icml.cc/](https://icml.cc/) / [https://iclr.cc/](https://iclr.cc/)\n- **Frequency**: Annual\n- **Focus**: Foundation models for robotics, RL for manipulation, world models\n- **Best For**: Tracking how mainstream ML advances are being applied to embodied AI.\n\n### 8. Papers With Code — Robotics\n- **URL**: [https://paperswithcode.com/area/robots](https://paperswithcode.com/area/robots)\n- **Frequency**: Continuously updated\n- **Focus**: Benchmarks, leaderboards, code implementations for robotics papers\n- **Key Strength**: Quickly find reproducible implementations of the latest research.\n\n### 9. Semantic Scholar / Google Scholar Alerts\n- **URL**: [https://www.semanticscholar.org/](https://www.semanticscholar.org/) / [https://scholar.google.com/](https://scholar.google.com/)\n- **Frequency**: Configurable alerts\n- **Focus**: Custom keyword tracking (e.g., \"embodied AI\", \"VLA\", \"robot foundation model\")\n- **Key Strength**: Personalized paper feeds based on your specific research interests.\n\n---\n\n## Tier 4: General Tech & Business Media (Context & Trends)\n\nThese mainstream outlets provide broader context on how embodied AI fits into the tech landscape.\n\n### 1. Wired — Robotics\n- **URL**: [https://www.wired.com/tag/robots/](https://www.wired.com/tag/robots/)\n- **Frequency**: Weekly\n- **Focus**: Long-form features on humanoid robots, societal impact, factory deployments\n- **Key Strength**: Narrative-driven journalism that contextualizes technology within society.\n\n### 2. Forbes — AI & Robotics\n- **URL**: [https://www.forbes.com/ai/](https://www.forbes.com/ai/)\n- **Frequency**: Daily\n- **Focus**: Market analysis, CEO interviews, investment trends\n- **Key Strength**: Business perspective on robotics valuations and market opportunities.\n\n### 3. Reuters / Bloomberg — Robotics Coverage\n- **URL**: [https://www.reuters.com/technology/](https://www.reuters.com/technology/)\n- **Frequency**: Daily\n- **Focus**: Breaking news on major deals, IPOs, factory deployments, geopolitics\n- **Key Strength**: Authoritative financial and geopolitical reporting on the robotics industry.\n\n### 4. MIT Technology Review\n- **URL**: [https://www.technologyreview.com/topic/robots-and-machines/](https://www.technologyreview.com/topic/robots-and-machines/)\n- **Frequency**: Weekly\n- **Focus**: Ethical implications, breakthrough analysis, long-term trends\n- **Key Strength**: Thoughtful, research-backed analysis from MIT's media arm.\n\n### 5. New Scientist — Robots\n- **URL**: [https://www.newscientist.com/subject/robots/](https://www.newscientist.com/subject/robots/)\n- **Frequency**: Weekly\n- **Focus**: Science-first reporting on robot capabilities, bio-inspired design\n- **Key Strength**: Bridges the gap between academic research and public understanding.\n\n### 6. The Verge / Ars Technica\n- **URL**: [https://www.theverge.com/robot](https://www.theverge.com/robot) / [https://arstechnica.com/tag/robots/](https://arstechnica.com/tag/robots/)\n- **Frequency**: Daily\n- **Focus**: Product announcements, demo reactions, consumer robotics\n- **Key Strength**: Fast, accessible coverage of major announcements.\n\n---\n\n## Tier 5: Podcasts & Video Channels (On-the-Go Learning)\n\n### 1. The Embodied AI Podcast\n- **Platform**: Apple Podcasts / Spotify\n- **URL**: [https://podcasts.apple.com/us/podcast/the-embodied-ai-podcast/id1609977196](https://podcasts.apple.com/us/podcast/the-embodied-ai-podcast/id1609977196)\n- **Focus**: Deep conversations on how AI learns through physical interaction\n- **Best For**: Commute-friendly deep dives into embodied intelligence philosophy and practice.\n\n### 2. The TWIML AI Podcast (This Week in Machine Learning)\n- **URL**: [https://twimlai.com/podcast/](https://twimlai.com/podcast/)\n- **Focus**: Expert interviews on ML research, frequently covers robot learning\n- **Best For**: Staying current on how ML breakthroughs translate to robotics.\n\n### 3. Robohub Podcast\n- **URL**: [https://robohub.org/podcast/](https://robohub.org/podcast/)\n- **Focus**: Academic robotics, interviews with lab directors and researchers\n- **Best For**: Understanding the research pipeline behind embodied AI.\n\n### 4. Lex Fridman Podcast\n- **URL**: [https://lexfridman.com/podcast/](https://lexfridman.com/podcast/)\n- **Focus**: Long-form interviews with robotics leaders (Boston Dynamics, Tesla AI, etc.)\n- **Best For**: Deep, multi-hour conversations with field pioneers.\n\n### 5. Rodney Brooks' Blog\n- **URL**: [https://rodneybrooks.com/blog/](https://rodneybrooks.com/blog/)\n- **Focus**: Grounded, skeptical analysis of robotics hype vs. reality\n- **Best For**: A reality check from one of the most experienced roboticists alive.\n\n### 6. YouTube Channels\n| Channel | Focus | URL |\n|---------|-------|-----|\n| **Two Minute Papers** | Visual summaries of robotics/AI papers | [youtube.com/@TwoMinutePapers](https://www.youtube.com/@TwoMinutePapers) |\n| **Yannic Kilcher** | In-depth ML paper reviews (incl. robotics) | [youtube.com/@YannicKilcher](https://www.youtube.com/@YannicKilcher) |\n| **AI for Good (ITU)** | Webinars on robotics for social impact | [youtube.com/@AIforGood](https://www.youtube.com/@AIforGood) |\n| **Boston Dynamics** | Official demo & research videos | [youtube.com/@BostonDynamics](https://www.youtube.com/@BostonDynamics) |\n\n---\n\n## Tier 6: Newsletters & Curated Digests\n\n### 1. The Batch (by Andrew Ng / DeepLearning.AI)\n- **URL**: [https://www.deeplearning.ai/the-batch/](https://www.deeplearning.ai/the-batch/)\n- **Frequency**: Weekly\n- **Focus**: Curated AI news with expert commentary, frequently covers robotics\n- **Best For**: A concise weekly summary with Andrew Ng's perspective.\n\n### 2. Import AI (by Jack Clark)\n- **URL**: [https://importai.substack.com/](https://importai.substack.com/)\n- **Frequency**: Weekly\n- **Focus**: AI policy, frontier research, embodied AI developments\n- **Best For**: Understanding the policy and safety implications of embodied AI.\n\n### 3. Last Week in AI\n- **URL**: [https://lastweekin.ai/](https://lastweekin.ai/)\n- **Frequency**: Weekly\n- **Focus**: Comprehensive AI news roundup including robotics\n- **Best For**: Catching anything you might have missed during the week.\n\n### 4. Automation & Robotics Newsletter (by The Robot Report)\n- **URL**: Subscribe at [therobotreport.com](https://www.therobotreport.com/)\n- **Frequency**: Daily / Weekly digest\n- **Focus**: Curated top stories from across the robotics industry.\n\n### 5. Nathan Benaich's \"State of AI\" Report\n- **URL**: [https://www.stateof.ai/](https://www.stateof.ai/)\n- **Frequency**: Annual\n- **Focus**: Comprehensive annual review of AI progress, including robotics/embodied AI section\n- **Best For**: Big-picture annual trend analysis.\n\n---\n\n## Tier 7: Chinese Ecosystem Sources (Real-Time Tracking)\n\nChina's embodied AI sector is evolving extremely fast. These channels offer excellent coverage of the domestic robotics supply chain.\n\n### 1. Synced (机器之心 — English)\n- **URL**: [https://syncedreview.com/](https://syncedreview.com/)\n- **Frequency**: Daily\n- **Focus**: Embodied AI algorithms, VLA models, Chinese robotics industry chain\n- **Key Strength**: One of the most professional AI media outlets in China; detailed breakdowns of papers and technical solutions.\n\n### 2. QbitAI (量子位)\n- **URL**: [https://www.qbitai.com/](https://www.qbitai.com/)\n- **Frequency**: Daily\n- **Focus**: Unitree, AGIBOT, UBTECH and other domestic humanoid robot updates\n- **Key Strength**: Vivid reporting style; excels at capturing hot topics in China's embodied AI circle.\n\n### 3. 机器人大讲堂 (Robot Lecture Hall)\n- **Platform**: WeChat Official Account (search \"机器人大讲堂\")\n- **Frequency**: Daily\n- **Focus**: Policy & regulation, investment & financing, domestic industrial park developments\n- **Best For**: Tracking China's robotics policy and commercial ecosystem.\n\n### 4. 36Kr — Robotics Section\n- **URL**: [https://36kr.com/](https://36kr.com/) (search robotics/机器人)\n- **Frequency**: Daily\n- **Focus**: Startup profiles, funding rounds, industry analysis\n- **Key Strength**: China's leading tech business media; strong coverage of robotics startups.\n\n### 5. CSET (Georgetown) — China Embodied AI Reports\n- **URL**: [https://cset.georgetown.edu/](https://cset.georgetown.edu/)\n- **Frequency**: Periodic reports\n- **Focus**: PRC government support for embodied AI, research infrastructure mapping\n- **Best For**: English-language analysis of China's embodied AI strategy and policy.\n\n---\n\n## Tier 8: Community & Discussion Forums\n\n### 1. r/robotics (Reddit)\n- **URL**: [https://www.reddit.com/r/robotics/](https://www.reddit.com/r/robotics/)\n- **Focus**: General robotics discussion, project showcases, career advice\n\n### 2. r/AskRobotics (Reddit)\n- **URL**: [https://www.reddit.com/r/AskRobotics/](https://www.reddit.com/r/AskRobotics/)\n- **Focus**: Q&A on humanoid robots, embodied AI, career paths\n\n### 3. Robotics Worldwide Mailing List\n- **Platform**: Email list (subscribe via academic channels)\n- **Focus**: Job postings, CFPs, academic announcements\n\n### 4. Hugging Face — Robotics\n- **URL**: [https://huggingface.co/blog](https://huggingface.co/blog) (search robotics)\n- **Focus**: Open-source robot models (LeRobot), datasets, community projects\n- **Key Strength**: The open-source hub for embodied AI models and datasets (e.g., LeRobot framework).\n\n### 5. ROS Discourse\n- **URL**: [https://discourse.ros.org/](https://discourse.ros.org/)\n- **Focus**: Robot Operating System ecosystem, middleware, developer tools\n- **Best For**: Hands-on robotics software development community.\n\n---\n\n## Tier 9: Major Conferences & Events (Calendar)\n\n| Event | Typical Timing | Focus | URL |\n|-------|---------------|-------|-----|\n| **NVIDIA GTC** | March | GPU-accelerated robotics, Isaac platform | [nvidia.com/gtc](https://www.nvidia.com/gtc/) |\n| **Google I/O** | May | DeepMind robotics demos, Android robotics | [io.google](https://io.google/) |\n| **ICRA** | May–June | Largest robotics academic conference | [ieee-ras.org](https://www.ieee-ras.org/) |\n| **RSS** | July | Top-tier robotics research | [roboticsconference.org](https://roboticsconference.org/) |\n| **RoboCup** | July | Robot competition & benchmarking | [robocup.org](https://www.robocup.org/) |\n| **IROS** | October | Intelligent robots & systems | [iros.org](https://www.iros.org/) |\n| **CoRL** | November | Robot learning (core embodied AI) | [corl.org](https://www.corl.org/) |\n| **NeurIPS** | December | ML + robotics workshops | [neurips.cc](https://neurips.cc/) |\n| **CES** | January | Consumer & humanoid robot demos | [ces.tech](https://www.ces.tech/) |\n| **Automate** | May (biennial) | Industrial automation & robotics expo | [automate.org](https://www.automate.org/) |\n| **World Robot Conference (WRC)** | August | China's flagship robotics expo | [worldrobotconference.com](http://www.worldrobotconference.com/) |\n\n---\n\n## 📋 Usage Guide\n\n### Daily Morning Routine\n1. Skim **The Robot Report** and **TechCrunch Robotics** for global business & funding headlines.\n2. Check **Tesla AI**, **Figure**, and **Unitree** social media for new demo videos.\n3. Glance at **QbitAI** or **36Kr** for China-specific developments.\n\n### Weekly Deep Dive\n1. Read **IEEE Spectrum** feature articles to understand the engineering challenges behind the headlines.\n2. Review **arXiv (cs.RO + cs.CV)** for widely discussed embodied AI papers of the week.\n3. Listen to one episode of **The Embodied AI Podcast** or **TWIML**.\n4. Read newsletters: **The Batch**, **Import AI**, **Last Week in AI**.\n\n### Monthly Review\n1. Check all **Tier 2** company blogs for product updates and deployment metrics.\n2. Review **Papers With Code** leaderboards for benchmark movements.\n3. Read **Rodney Brooks' blog** for a grounded reality check.\n\n### Quarterly Trend Assessment\n1. Follow major conferences: **NVIDIA GTC**, **Google I/O**, **ICRA**, **CoRL**.\n2. Compile data from **Tier 2** company blogs on \"units deployed\" and \"task success rates.\"\n3. Review **CSET reports** for geopolitical dynamics in embodied AI.\n4. Check **State of AI Report** (annual) for macro trends.\n\n---\n\n> **Last Updated**: February 2026\n\nFile v1.0.5:references/output_templates.md\n\n# 🤖 Embodied AI News — Output Format Templates\n\nPre-defined templates tailored for the Embodied AI / Robotics domain, covering hardware breakthroughs, foundation models, sim-to-real transfer, commercial deployments, and supply chain dynamics.\n\n---\n\n## Standard Format (Default)\n\nThe most commonly used format with Embodied AI–specific categories.\n\n```markdown\n# 🤖 Embodied AI Daily Briefing\n\n**Date**: [Current Date, e.g., February 23, 2026]\n**Sources**: [X] articles from [Y] sources\n**Coverage**: Last 24 hours\n\n---\n\n## 🔥 Major Announcements\n\n### [Headline 1]\n\n**Summary**: [One-sentence overview of the news]\n\n**Key Points**:\n- [Important detail 1]\n- [Important detail 2]\n- [Important detail 3]\n\n**Robot/Platform**: [e.g., Optimus Gen-3 / Digit v3 / GR00T — or \"N/A\" if not platform-specific]\n**Tech Stack**: [e.g., VLA Model + Dexterous Hand / RL Policy + Sim-to-Real / End-to-End Transformer]\n**Impact**: [Why this matters for the embodied AI field — 1-2 sentences]\n\n📅 **Source**: [Publication Name] • [Publication Date]\n🔗 **Link**: [URL]\n\n---\n\n### [Headline 2]\n\n**Summary**: [One-sentence overview]\n\n**Key Points**:\n- [Important detail 1]\n- [Important detail 2]\n\n**Robot/Platform**: [Platform name or \"N/A\"]\n**Tech Stack**: [Key technologies involved]\n**Impact**: [Why this matters]\n\n📅 **Source**: [Publication Name] • [Publication Date]\n🔗 **Link**: [URL]\n\n---\n\n## 🧠 Foundation Models & Algorithms\n\n### [Headline 3]\n\n**Summary**: [One-sentence overview]\n\n**Key Points**:\n- [Important detail 1]\n- [Important detail 2]\n- [Important detail 3]\n\n**Model Type**: [VLA / World Model / Diffusion Policy / RL / Imitation Learning / Other]\n**Embodiment**: [Humanoid / Manipulator / Quadruped / Multi-embodiment / Simulation-only]\n**Open Source**: [Yes — GitHub link / No / Partial]\n**Impact**: [Why this matters]\n\n📅 **Source**: [Publication Name] • [Publication Date]\n🔗 **Link**: [URL]\n\n---\n\n## 🦾 Hardware & Platforms\n\n### [Headline 4]\n\n**Summary**: [One-sentence overview]\n\n**Key Points**:\n- [Important detail 1]\n- [Important detail 2]\n\n**Hardware Type**: [Humanoid / Dexterous Hand / Actuator / Sensor / Compute Module / Full Platform]\n**Company**: [Company name]\n**Specs**: [Key specs if available — DoF, payload, battery life, etc.]\n**Impact**: [Why this matters]\n\n📅 **Source**: [Publication Name] • [Publication Date]\n🔗 **Link**: [URL]\n\n---\n\n## 🏭 Deployments & Commercial\n\n### [Headline 5]\n\n**Summary**: [One-sentence overview]\n\n**Key Points**:\n- [Important detail 1]\n- [Important detail 2]\n\n**Deployment Scale**: [Pilot / Small batch / Mass production — units if known]\n**Industry Vertical**: [Automotive / Logistics / Manufacturing / Household / Agriculture / Other]\n**Company → Customer**: [e.g., Figure AI → BMW, Agility → Amazon]\n**Impact**: [Why this matters]\n\n📅 **Source**: [Publication Name] • [Publication Date]\n🔗 **Link**: [URL]\n\n---\n\n## 🌐 Simulation & Infrastructure\n\n### [Headline 6]\n\n**Summary**: [One-sentence overview]\n\n**Key Points**:\n- [Important detail 1]\n- [Important detail 2]\n\n**Platform**: [Isaac Sim / MuJoCo / Habitat / SAPIEN / Genesis / Other]\n**Use Case**: [Sim-to-Real Transfer / Data Generation / Benchmarking / Digital Twin]\n**Impact**: [Why this matters]\n\n📅 **Source**: [Publication Name] • [Publication Date]\n🔗 **Link**: [URL]\n\n---\n\n## 💰 Funding & M&A\n\n### [Headline 7]\n\n**Summary**: [One-sentence overview]\n\n**Key Points**:\n- [Amount raised / Deal value]\n- [Lead investors]\n- [Intended use of funds]\n\n**Company**: [Company name]\n**Valuation**: [Post-money valuation if known]\n**Stage**: [Seed / Series A / B / C / D+ / IPO / Acquisition]\n**Impact**: [Why this matters for the ecosystem]\n\n📅 **Source**: [Publication Name] • [Publication Date]\n🔗 **Link**: [URL]\n\n---\n\n## 🌍 Policy, Safety & Ethics\n\n### [Headline 8]\n\n**Summary**: [One-sentence overview]\n\n**Key Points**:\n- [Important detail 1]\n- [Important detail 2]\n\n**Region**: [US / EU / China / Global]\n**Policy Type**: [Regulation / Standard / Guideline / Export Control / Safety Framework]\n**Impact**: [Why this matters]\n\n📅 **Source**: [Publication Name] • [Publication Date]\n🔗 **Link**: [URL]\n\n---\n\n## 🇨🇳 China Ecosystem Spotlight\n\n### [Headline 9]\n\n**Summary**: [One-sentence overview]\n\n**Key Points**:\n- [Important detail 1]\n- [Important detail 2]\n\n**Company/Institution**: [e.g., Unitree / AGIBOT / UBTECH / Galbot / Tsinghua IIIS]\n**Segment**: [Humanoid / Quadruped / Industrial Arm / Supply Chain Component / Policy]\n**Impact**: [Why this matters]\n\n📅 **Source**: [Publication Name] • [Publication Date]\n🔗 **Link**: [URL]\n\n---\n\n## ⭐ GitHub 热门开源（具身智能相关）\n\n> Include this section only when **`github_repos.md`** / **Recipe F** was executed. Place **above** Key Takeaways. **5–8** repos; verified `https://github.com/owner/repo` links only.\n\n**Snapshot**: [As of date/time; e.g. “metrics from live GitHub pages at briefing time”]\n\n| # | Repository | Category | Stars (if verified) | One-line role |\n|---|------------|----------|---------------------|---------------|\n| 1 | [`owner/repo`](https://github.com/owner/repo) | Policy / VLA | [e.g. 12.4k] | [≤20 words] |\n| 2 | [`owner/repo`](https://github.com/owner/repo) | Sim & Sim2Real | [see repo] | [≤20 words] |\n| 3 | [`owner/repo`](https://github.com/owner/repo) | Data / Teleop | … | … |\n| 4 | … | … | … | … |\n\n**Worth watching**: [1–2 sentences — e.g. emerging repo with high recent activity, or successor to an archived stack]\n\n**Note**: Rankings combine ecosystem role, recency, and stars (see `github_repos.md`); not a definitive global order.\n\n---\n\n## 🎯 Key Takeaways\n\n1. [The biggest news of the day — 1 sentence]\n2. [Second most important development — 1 sentence]\n3. [An emerging trend worth watching — 1 sentence]\n\n---\n\n## 📊 Daily Pulse\n\n| Metric | Value |\n|--------|-------|\n| **Total stories analyzed** | [X] |\n| **Most active companies** | [Top 3] |\n| **Hottest tech topic** | [e.g., VLA, Dexterous Manipulation, Sim-to-Real] |\n| **Hardware vs. Software** | [X]% hardware, [Y]% software |\n| **US vs. China coverage** | [X]% US, [Y]% China, [Z]% Other |\n| **Sentiment** | [Bullish / Neutral / Cautious] |\n\n---\n\n**Generated on**: [Timestamp]\n**Next update**: Check back tomorrow for the latest Embodied AI news\n```\n\n---\n\n## Brief Format (Headlines Only)\n\nQuick scan format for busy engineers and investors.\n\n```markdown\n# 🤖 Embodied AI Headlines\n\n**Date**: [Current Date]\n**Coverage**: Last 24 hours\n\n## 🔥 Major Announcements\n\n• [Headline 1] — *[Company/Lab]* ([Publication])\n🔗 [URL]\n\n• [Headline 2] — *[Company/Lab]* ([Publication])\n🔗 [URL]\n\n---\n\n## 🧠 Foundation Models & Algorithms\n\n• [Headline 3] — *[Model Name]* ([Publication])\n🔗 [URL]\n\n• [Headline 4] — *[Model Name]* ([Publication])\n🔗 [URL]\n\n---\n\n## 🦾 Hardware & Platforms\n\n• [Headline 5] — *[Robot/Component]* ([Publication])\n🔗 [URL]\n\n---\n\n## 🏭 Deployments & Commercial\n\n• [Headline 6] — *[Company → Customer]* ([Publication])\n🔗 [URL]\n\n---\n\n## 💰 Funding & M&A\n\n• [Headline 7] — *[Company] raises $[X]M* ([Publication])\n🔗 [URL]\n\n---\n\n## 🇨🇳 China Ecosystem\n\n• [Headline 8] — *[Company/Lab]* ([Publication])\n🔗 [URL]\n\n---\n\n## ⭐ GitHub 热门开源（可选）\n\n> Omit this entire section if the GitHub module was not run.\n\n• [`owner/repo`](https://github.com/owner/repo) — *[Category tag]* — [≤15 words]\n• [`owner/repo`](https://github.com/owner/repo) — *[Category tag]* — [≤15 words]\n• *(3–6 more as needed; 5–8 total)*\n\n---\n\n**Quick Summary**: [2-3 sentence overview of the day's most important Embodied AI news]\n\n**Generated on**: [Timestamp]\n```\n\n---\n\n## Deep Format (With Analysis)\n\nComprehensive format with in-depth technical analysis and strategic implications.\n\n```markdown\n# 📊 Embodied AI Deep Dive\n\n**Date**: [Current Date]\n**Coverage**: Last 24 hours\n**Analysis Depth**: In-depth\n\n---\n\n## 🔥 Major Announcements\n\n### [Headline 1]\n\n**Summary**: [One-sentence overview]\n\n**Key Details**:\n- [Important detail 1]\n- [Important detail 2]\n- [Important detail 3]\n- [Additional detail 4]\n- [Additional detail 5]\n\n**Technical Deep Dive**:\n[2-3 sentences breaking down the technical approach — architecture, training paradigm, key innovations]\n\n**Benchmark / Performance**:\n| Metric | This Work | Previous SOTA | Improvement |\n|--------|-----------|---------------|-------------|\n| [Metric 1] | [Value] | [Value] | [+X%] |\n| [Metric 2] | [Value] | [Value] | [+X%] |\n\n**Impact Analysis**:\n[2-3 sentences analyzing why this matters and its implications for the field]\n\n**Expert Reactions**:\n[Summary of expert opinions from X/Twitter, blog posts, or interviews]\n\n**Context & Background**:\n[How this fits into the broader trajectory — previous work, competing approaches, timeline]\n\n📅 **Source**: [Publication Name] • [Publication Date]\n🔗 **Link**: [URL]\n\n---\n\n## 🧠 Foundation Models & Algorithms\n\n### [Paper/Model Title]\n\n**Summary**: [What this model/algorithm does]\n\n**Architecture**:\n- **Input**: [Vision / Language / Proprioception / Tactile / Multi-modal]\n- **Backbone**: [Transformer / Diffusion / Flow Matching / RL / Hybrid]\n- **Output**: [Actions / Trajectories / Plans / Rewards]\n- **Training**: [Imitation Learning / RL / Self-supervised / Hybrid — data scale if known]\n\n**Key Contributions**:\n- [Contribution 1]\n- [Contribution 2]\n- [Contribution 3]\n\n**Embodiment & Tasks**:\n- **Tested on**: [Robot platform(s)]\n- **Tasks**: [Pick-and-place / Navigation / Dexterous manipulation / Locomotion / Multi-task]\n- **Sim-to-Real**: [Yes/No — transfer method if applicable]\n\n**Results**:\n[Key benchmarks, success rates, generalization capabilities]\n\n**Limitations & Open Questions**:\n[What doesn't work yet, failure modes, scalability concerns]\n\n**Significance**:\n[Why this advances the state of embodied AI — comparison to prior work]\n\n📅 **Source**: [Publication/Venue] • [Publication Date]\n🔗 **Paper**: [arXiv/PDF link]\n💻 **Code**: [GitHub link if available]\n🎬 **Demo**: [Video link if available]\n\n---\n\n## 🦾 Hardware & Platforms\n\n### [Robot/Component Name]\n\n**Summary**: [What's new]\n\n**Specifications**:\n| Spec | Value |\n|------|-------|\n| **DoF** | [X] |\n| **Height / Weight** | [X cm / X kg] |\n| **Payload** | [X kg] |\n| **Battery Life** | [X hours] |\n| **Compute** | [Onboard chip / edge device] |\n| **Sensors** | [Cameras, LiDAR, tactile, IMU, etc.] |\n| **Actuators** | [Type — electric, hydraulic, quasi-direct-drive, etc.] |\n| **Price Point** | [If disclosed] |\n\n**Key Innovations**:\n- [Innovation 1]\n- [Innovation 2]\n\n**Comparison to Competitors**:\n[How this stacks up against similar platforms]\n\n**Supply Chain Notes**:\n[Key component suppliers, manufacturing approach, production capacity]\n\n📅 **Source**: [Publication Name] • [Publication Date]\n🔗 **Link**: [URL]\n\n---\n\n## 🏭 Deployments & Commercial\n\n### [Headline]\n\n**Summary**: [One-sentence overview]\n\n**Deployment Details**:\n- **Company**: [Robot maker]\n- **Customer**: [End user / Factory]\n- **Scale**: [Number of units / pilot vs. production]\n- **Tasks**: [What the robots are doing]\n- **Location**: [Factory / Warehouse / City]\n- **Timeline**: [Start date, ramp plan]\n\n**Performance Metrics** (if disclosed):\n- **Task Success Rate**: [X%]\n- **Uptime**: [X%]\n- **Throughput**: [X units/hour]\n- **ROI Timeline**: [X months]\n\n**Market Implications**:\n[What this means for the industry — TAM, competitive positioning, adoption curve]\n\n📅 **Source**: [Publication Name] • [Publication Date]\n🔗 **Link**: [URL]\n\n---\n\n## 💰 Funding & M&A\n\n### [Company Name] — [Round Type]\n\n**Summary**: [One-sentence overview]\n\n**Deal Details**:\n- **Amount**: $[X]M\n- **Valuation**: $[X]B (post-money)\n- **Lead Investors**: [Names]\n- **Notable Participants**: [Strategic investors — e.g., NVIDIA, Amazon, Hyundai]\n- **Use of Funds**: [R&D / Manufacturing scale-up / Hiring / Market expansion]\n\n**Company Profile**:\n- **Founded**: [Year]\n- **HQ**: [Location]\n- **Employees**: [~X]\n- **Core Product**: [Robot/Platform name]\n- **Total Raised to Date**: $[X]M\n\n**Market Context**:\n[How this round compares to peers, what it signals about investor appetite]\n\n📅 **Source**: [Publication Name] • [Publication Date]\n🔗 **Link**: [URL]\n\n---\n\n## 🌍 Policy, Safety & Ethics\n\n### [Headline]\n\n**Summary**: [What's happening]\n\n**Key Points**:\n- [Point 1]\n- [Point 2]\n- [Point 3]\n\n**Stakeholder Perspectives**:\n- **Industry**: [How companies are reacting]\n- **Researchers**: [Academic community response]\n- **Regulators**: [Government stance]\n- **Public**: [Societal concerns]\n\n**Implications for Embodied AI Development**:\n[What this means for robot deployment timelines, safety requirements, export controls]\n\n📅 **Source**: [Publication Name] • [Publication Date]\n🔗 **Link**: [URL]\n\n---\n\n## ⭐ GitHub 热门开源（具身智能相关）\n\n> Optional; same table schema as **Standard Format**. Prefer **8–12** repos for weekly/monthly Deep briefings, still verified URLs only.\n\n| # | Repository | Category | Stars (if verified) | One-line role |\n|---|------------|----------|---------------------|---------------|\n| 1 | [`owner/repo`](https://github.com/owner/repo) | … | … | … |\n\n**Open-source momentum**: [2–4 bullets tying repos to themes above — e.g. sim stack consolidation, VLA training tooling, dataset releases]\n\n---\n\n## 🎯 Analysis & Insights\n\n### Biggest Story of the Day\n\n**[Headline]**: [3-4 sentences of analysis on why this is the most significant news and what it signals]\n\n### Emerging Trends\n\n1. **[Trend 1]**: [Description with examples from today's news]\n2. **[Trend 2]**: [Description with examples]\n3. **[Trend 3]**: [Description with examples]\n\n### Technology Convergence Map\n\n[Describe how today's news items connect — e.g., \"Company X's new VLA model + Company Y's dexterous hand + Company Z's sim platform = a new capability stack emerging\"]\n\n### What to Watch\n\n- **This week**: [Upcoming demos, conferences, earnings]\n- **This month**: [Product launches, paper deadlines, policy decisions]\n- **This quarter**: [Major conferences (ICRA, GTC, etc.), deployment milestones]\n\n---\n\n## 📊 Daily Pulse\n\n| Metric | Value |\n|--------|-------|\n| **Total stories analyzed** | [X] |\n| **Most active companies** | [Top 3] |\n| **Hottest tech topic** | [e.g., VLA, Dexterous Manipulation] |\n| **Hardware vs. Software split** | [X]% / [Y]% |\n| **US vs. China coverage** | [X]% / [Y]% |\n| **Research vs. Commercial** | [X]% / [Y]% |\n| **Sentiment** | [Bullish / Neutral / Cautious] |\n\n---\n\n**Generated on**: [Timestamp]\n**Next update**: Check back tomorrow for the latest Embodied AI news\n\nWant to dive deeper into any story? Just ask!\n```\n\n---\n\n## By-Company Format\n\nStories organized by company — ideal for competitive intelligence.\n\n```markdown\n# 🤖 Embodied AI News by Company\n\n**Date**: [Current Date]\n**Coverage**: Last 24 hours\n**Format**: By Company\n\n---\n\n## Tesla (Optimus)\n\n### 🔥 [Headline 1]\n**Summary**: [One-sentence overview]\n**Tech**: [Key technology involved]\n🔗 [URL]\n\n---\n\n## Figure AI\n\n### [Headline 2]\n**Summary**: [One-sentence overview]\n**Tech**: [Key technology involved]\n🔗 [URL]\n\n---\n\n## Boston Dynamics\n\n### [Headline 3]\n**Summary**: [One-sentence overview]\n**Tech**: [Key technology involved]\n🔗 [URL]\n\n---\n\n## NVIDIA (Isaac / GR00T)\n\n### [Headline 4]\n**Summary**: [One-sentence overview]\n**Tech**: [Key technology involved]\n🔗 [URL]\n\n---\n\n## Google DeepMind\n\n### [Headline 5]\n**Summary**: [One-sentence overview]\n**Tech**: [Key technology involved]\n🔗 [URL]\n\n---\n\n## 1X Technologies\n\n### [Headline 6]\n**Summary**: [One-sentence overview]\n**Tech**: [Key technology involved]\n🔗 [URL]\n\n---\n\n## Unitree Robotics (宇树)\n\n### [Headline 7]\n**Summary**: [One-sentence overview]\n**Tech**: [Key technology involved]\n🔗 [URL]\n\n---\n\n## AGIBOT (智元)\n\n### [Headline 8]\n**Summary**: [One-sentence overview]\n**Tech**: [Key technology involved]\n🔗 [URL]\n\n---\n\n## Physical Intelligence (π)\n\n### [Headline 9]\n**Summary**: [One-sentence overview]\n**Tech**: [Key technology involved]\n🔗 [URL]\n\n---\n\n## Other Companies\n\n### 💰 [Headline 10 — Company Name]\n**Summary**: [One-sentence overview]\n🔗 [URL]\n\n### 🦾 [Headline 11 — Company Name]\n**Summary**: [One-sentence overview]\n🔗 [URL]\n\n---\n\n## 🎯 Key Takeaways\n\n1. [Most significant company news — 1 sentence]\n2. [Company to watch — 1 sentence]\n3. [Competitive dynamic to track — 1 sentence]\n\n---\n\n**Generated on**: [Timestamp]\n```\n\n---\n\n## Research-Only Format\n\nFocus exclusively on Embodied AI papers and academic breakthroughs.\n\n```markdown\n# 🔬 Embodied AI Research Weekly\n\n**Date**: [Current Date]\n**Coverage**: Last 7 days\n**Focus**: Research Papers Only\n\n---\n\n## 🏆 Paper of the Week\n\n### [Paper Title]\n\n**Authors**: [Author names]\n**Institution**: [Institution(s)]\n**Venue**: [CoRL / ICRA / RSS / NeurIPS / arXiv preprint]\n\n**Abstract Summary**: [2-3 sentences summarizing the paper]\n\n**Architecture & Method**:\n- **Approach**: [VLA / Diffusion Policy / World Model / RL / Hybrid]\n- **Input Modalities**: [RGB / Depth / Language / Proprioception / Tactile / Point Cloud]\n- **Action Space**: [Continuous / Discrete / Hybrid — dimensionality]\n- **Training Data**: [Human demos / Sim data / Internet video / Hybrid — scale]\n\n**Key Contributions**:\n- [Contribution 1]\n- [Contribution 2]\n- [Contribution 3]\n\n**Experimental Setup**:\n- **Robot(s)**: [Platform name(s)]\n- **Tasks**: [Task descriptions]\n- **Sim/Real**: [Simulation only / Real-world / Sim-to-Real transfer]\n\n**Results**:\n| Benchmark / Task | This Work | Previous SOTA | Δ |\n|------------------|-----------|---------------|---|\n| [Task 1] | [X%] | [Y%] | [+Z%] |\n| [Task 2] | [X%] | [Y%] | [+Z%] |\n\n**Limitations**:\n- [Limitation 1]\n- [Limitation 2]\n\n**Significance**:\n[Why this paper matters — 2-3 sentences on impact and future directions]\n\n📅 **Posted**: [Date]\n🔗 **Paper**: [arXiv link]\n💻 **Code**: [GitHub link if available]\n🎬 **Demo Video**: [URL if available]\n📦 **Dataset**: [URL if released]\n\n---\n\n## Notable Papers\n\n### 🥈 [Paper Title 2]\n\n**Authors**: [Author names] • **Institution**: [Institution(s)]\n**Topic**: [e.g., Dexterous Manipulation / Locomotion / Navigation / Multi-task]\n\n**TL;DR**: [1-2 sentence summary]\n\n**Key Innovation**: [The single most important contribution]\n\n**Results Highlight**: [Most impressive result in one line]\n\n🔗 **Paper**: [arXiv link]\n💻 **Code**: [GitHub link if available]\n\n---\n\n### 🥉 [Paper Title 3]\n\n**Authors**: [Author names] • **Institution**: [Institution(s)]\n**Topic**: [Topic area]\n\n**TL;DR**: [1-2 sentence summary]\n\n**Key Innovation**: [The single most important contribution]\n\n**Results Highlight**: [Most impressive result in one line]\n\n🔗 **Paper**: [arXiv link]\n💻 **Code**: [GitHub link if available]\n\n---\n\n### [Paper Title 4]\n\n**Authors**: [Author names] • **Institution**: [Institution(s)]\n**Topic**: [Topic area]\n\n**TL;DR**: [1-2 sentence summary]\n\n**Key Innovation**: [The single most important contribution]\n\n🔗 **Paper**: [arXiv link]\n\n---\n\n### [Paper Title 5]\n\n**Authors**: [Author names] • **Institution**: [Institution(s)]\n**Topic**: [Topic area]\n\n**TL;DR**: [1-2 sentence summary]\n\n**Key Innovation**: [The single most important contribution]\n\n🔗 **Paper**: [arXiv link]\n\n---\n\n## 📈 Trending Research Topics This Week\n\n### [Topic 1 — e.g., \"Vision-Language-Action Models\"]\n**Papers this week**: [X]\n**Key trend**: [What's changing in this area]\n**Notable**: [Most important paper or result]\n\n### [Topic 2 — e.g., \"Sim-to-Real Transfer\"]\n**Papers this week**: [X]\n**Key trend**: [What's changing]\n**Notable**: [Most important paper or result]\n\n### [Topic 3 — e.g., \"Dexterous Manipulation\"]\n**Papers this week**: [X]\n**Key trend**: [What's changing]\n**Notable**: [Most important paper or result]\n\n---\n\n## 🎯 Research Insights\n\n1. **Most significant breakthrough**: [Description]\n2. **Emerging research direction**: [Description]\n3. **Technique gaining traction**: [Description]\n4. **Under-explored opportunity**: [Description]\n\n---\n\n## 📊 Statistics\n\n| Metric | Value |\n|--------|-------|\n| **Total papers covered** | [X] |\n| **Top institutions** | [Top 3] |\n| **Most active research areas** | [Top 3] |\n| **Papers with open-source code** | [X]% |\n| **Sim-only vs. Real-world** | [X]% / [Y]% |\n| **Single-task vs. Multi-task** | [X]% / [Y]% |\n\n---\n\n**Generated on**: [Timestamp]\n```\n\n---\n\n## Supply Chain & Hardware Format\n\nUnique to Embodied AI — tracks the physical component ecosystem.\n\n```markdown\n# ⚙️ Embodied AI Hardware & Supply Chain Tracker\n\n**Date**: [Current Date]\n**Coverage**: Last 7 days\n**Focus**: Hardware, Components, Manufacturing\n\n---\n\n## 🦾 New Robot Platforms\n\n### [Robot Name — Company]\n\n**Type**: [Humanoid / Quadruped / Manipulator / Mobile Manipulator]\n\n**Key Specs**:\n| Spec | Value |\n|------|-------|\n| Height / Weight | [X] |\n| DoF | [X] |\n| Payload | [X kg] |\n| Max Speed | [X m/s] |\n| Battery | [X Wh / X hours] |\n| Onboard Compute | [Chip model] |\n| Price | [If disclosed] |\n\n**What's New**: [Key improvements over previous generation]\n\n🔗 [URL]\n\n---\n\n## 🖐️ End Effectors & Dexterous Hands\n\n### [Product Name — Company]\n\n**Specs**: [DoF, grip force, tactile sensors, weight]\n**Innovation**: [What makes this notable]\n**Compatible Platforms**: [Which robots it works with]\n\n🔗 [URL]\n\n---\n\n## 🔧 Actuators & Motors\n\n### [Product/Development — Company]\n\n**Type**: [Quasi-direct-drive / Harmonic / Planetary / Hydraulic / SEA]\n**Specs**: [Torque, weight, bandwidth, efficiency]\n**Significance**: [Why this matters for embodied AI]\n\n🔗 [URL]\n\n---\n\n## 👁️ Sensors & Perception Hardware\n\n### [Product — Company]\n\n**Type**: [Depth Camera / LiDAR / Tactile Sensor / IMU / Force-Torque]\n**Specs**: [Resolution, range, refresh rate, etc.]\n**Use Case**: [How it's being used in embodied AI]\n\n🔗 [URL]\n\n---\n\n## 💻 Compute & Edge AI\n\n### [Chip/Module — Company]\n\n**Specs**: [TOPS, power consumption, form factor]\n**Target**: [Onboard robot inference / Cloud offload / Hybrid]\n**Significance**: [Performance vs. power trade-off implications]\n\n🔗 [URL]\n\n---\n\n## �icing Supply Chain Moves\n\n### [Headline — Company]\n\n**Type**: [New factory / Partnership / Vertical integration / Component shortage]\n**Details**: [Key facts]\n**Impact on Embodied AI**: [How this affects robot cost, availability, or capability]\n\n🔗 [URL]\n\n---\n\n## 📊 Market Snapshot\n\n| Metric | Value |\n|--------|-------|\n| **Humanoid robots shipped (est.)** | [X units YTD] |\n| **Average humanoid price trend** | [↑/↓/→ vs. last quarter] |\n| **Key component bottleneck** | [e.g., Harmonic drives, tactile sensors] |\n| **Most active hardware region** | [US / China / Japan / Korea] |\n\n---\n\n**Generated on**: [Timestamp]\n```\n\n---\n\n## Template Selection Guide\n\n| Format | Use When | Length | Detail Level | Unique Value |\n|--------|----------|--------|--------------|--------------|\n| **Standard** | Default daily briefing | Medium | Summaries + key points | Balanced across all categories |\n| **Brief** | Quick scan, time-constrained | Short | Headlines only | 30-second overview |\n| **Deep** | Research & strategy sessions | Long | Full analysis + benchmarks | Technical depth + market context |\n| **By-Company** | Competitive intelligence | Medium | Company-organized | Track specific players |\n| **Research-Only** | Academic focus, R&D teams | Medium | Papers + benchmarks | Reproducibility focus |\n| **Supply Chain** | Hardware teams, investors | Medium | Specs + components | Physical ecosystem tracking |\n\n---\n\n## Customization Elements\n\nThese modular sections can be added to **any** template above:\n\n### 🎬 Demo of the Day\n```markdown\n## 🎬 Demo of the Day\n**[Title]**: [1-sentence description]\n🎥 **Video**: [URL]\n**Why it matters**: [1 sentence]\n```\n\n### 📐 Architecture Diagram Reference\n```markdown\n## 📐 Architecture Spotlight\n**Model**: [Name]\n**Pipeline**: [Input] → [Encoder] → [Policy/Planner] → [Action Decoder] → [Robot]\n**Key Insight**: [What's novel about this architecture]\n```\n\n### 🗺️ Geo Tracker\n```markdown\n## 🗺️ Geographic Activity\n- **🇺🇸 US**: [X stories — top: Company A, Company B]\n- **🇨🇳 China**: [X stories — top: Company C, Company D]\n- **🇪🇺 EU**: [X stories — top: Company E]\n- **🇯🇵 Japan / 🇰🇷 Korea**: [X stories]\n```\n\n### 📅 Upcoming Events\n```markdown\n## 📅 Coming Up\n- **[Date]**: [Event name — what to expect]\n- **[Date]**: [Event name — what to expect]\n```\n\n### 🔗 Cross-Reference Tags\n```markdown\n**Tags**: #VLA #HumanoidRobot #SimToReal #DexterousManipulation #FoundationModel #Deployment #Funding #China #OpenSource\n```\n\n---\n\n## Notes for Template Users\n\n1. **Category flexibility**: Not every daily briefing will have news in all categories. Skip empty sections rather than forcing filler content.\n2. **China section**: Can be merged into main categories if there's only 1-2 China-specific stories; break it out when there are 3+ stories.\n3. **Demo videos**: Embodied AI is uniquely visual. Always include video links when available — they often convey more than text.\n4. **Benchmark tables**: Use sparingly in Standard format but always include in Deep and Research formats.\n5. **Supply Chain format**: Best used as a weekly or bi-weekly supplement rather than daily.\n\nFile v1.0.5:references/search_queries.md\n\n# 🔍 Embodied AI — Search Query Templates\n\nPre-defined search query templates for discovering Embodied AI news across hardware, algorithms, deployments, funding, and academic research.\n\n---\n\n## Date Variables\n\nUse dynamic date insertion based on current date:\n- **Today**: `[today]` (e.g., 2026-02-23)\n- **Yesterday**: `[today - 1d]` (e.g., 2026-02-22)\n- **This week**: `[today - 7d]` (e.g., 2026-02-16)\n- **This month**: `[today - 30d]` (e.g., 2026-01-24)\n\n---\n\n## 1. General Embodied AI News\n\n### 1.1 Daily Overview\n```\n(\"embodied AI\" OR \"embodied intelligence\" OR \"humanoid robot\") AND (\"news\" OR \"announcement\" OR \"launch\") after:[today - 1d]\n```\n\n### 1.2 Weekly Roundup\n```\n(\"embodied AI\" OR \"robot learning\" OR \"humanoid robot\") AND (\"breakthrough\" OR \"milestone\" OR \"update\") after:[today - 7d]\n```\n\n### 1.3 Broad Robotics + AI Intersection\n```\n(\"robot foundation model\" OR \"robotics AI\" OR \"physical AI\" OR \"physical intelligence\") after:[today - 1d]\n```\n\n### 1.4 Noise Exclusion Filter\n> Append to any query above to reduce irrelevant results:\n```\nNOT \"Roomba\" NOT \"chatbot\" NOT \"crypto\" NOT \"blockchain\" NOT \"RPA\" NOT \"software robot\"\n```\n\n---\n\n## 2. Foundation Models & Algorithms\n\n### 2.1 Vision-Language-Action (VLA) Models\n```\n(\"vision language action\" OR \"VLA model\" OR \"VLA policy\") AND (\"robot\" OR \"embodied\") after:[today - 7d]\n```\n\n### 2.2 Diffusion Policy & Flow Matching\n```\n(\"diffusion policy\" OR \"action diffusion\" OR \"flow matching policy\") AND (\"robot\" OR \"manipulation\") after:[today - 7d]\n```\n\n### 2.3 World Models for Robotics\n```\n(\"world model\" OR \"video prediction model\") AND (\"robot\" OR \"embodied\" OR \"physical\") after:[today - 7d]\n```\n\n### 2.4 Imitation Learning & Teleoperation Data\n```\n(\"imitation learning\" OR \"learning from demonstration\" OR \"teleoperation\" OR \"human demonstration\") AND (\"robot\") after:[today - 7d]\n```\n\n### 2.5 Reinforcement Learning for Robotics\n```\n(\"reinforcement learning\" OR \"RL policy\" OR \"reward shaping\") AND (\"robot\" OR \"locomotion\" OR \"manipulation\") after:[today - 7d]\n```\n\n### 2.6 Sim-to-Real Transfer\n```\n(\"sim-to-real\" OR \"sim2real\" OR \"domain randomization\" OR \"simulation transfer\") AND (\"robot\") after:[today - 7d]\n```\n\n### 2.7 End-to-End Robot Control\n```\n(\"end-to-end\" OR \"visuomotor policy\" OR \"sensorimotor\") AND (\"robot control\" OR \"robot learning\") after:[today - 7d]\n```\n\n### 2.8 Large Behavior Models / Generalist Policies\n```\n(\"generalist robot\" OR \"generalist policy\" OR \"large behavior model\" OR \"cross-embodiment\") after:[today - 7d]\n```\n\n### 2.9 3D / Spatial Understanding\n```\n(\"3D scene understanding\" OR \"spatial reasoning\" OR \"NeRF\" OR \"3D Gaussian\" OR \"point cloud\") AND (\"robot\" OR \"embodied\") after:[today - 7d]\n```\n\n### 2.10 Language-Conditioned Robotics\n```\n(\"language conditioned\" OR \"instruction following\" OR \"LLM planning\") AND (\"robot\" OR \"manipulation\" OR \"navigation\") after:[today - 7d]\n```\n\n---\n\n## 3. Hardware & Platforms\n\n### 3.1 Humanoid Robots (General)\n```\n(\"humanoid robot\" OR \"bipedal robot\" OR \"full-body humanoid\") AND (\"new\" OR \"launch\" OR \"update\" OR \"demo\") after:[today - 7d]\n```\n\n### 3.2 Dexterous Hands & Grippers\n```\n(\"dexterous hand\" OR \"robot hand\" OR \"robotic gripper\" OR \"tactile manipulation\") AND (\"new\" OR \"breakthrough\" OR \"launch\") after:[today - 7d]\n```\n\n### 3.3 Actuators & Motors\n```\n(\"robot actuator\" OR \"quasi-direct-drive\" OR \"harmonic drive\" OR \"electric actuator\" OR \"torque motor\") AND (\"new\" OR \"breakthrough\") after:[today - 7d]\n```\n\n### 3.4 Tactile & Force Sensors\n```\n(\"tactile sensor\" OR \"force torque sensor\" OR \"GelSight\" OR \"tactile sensing\") AND (\"robot\") after:[today - 7d]\n```\n\n### 3.5 Quadruped / Legged Robots\n```\n(\"quadruped robot\" OR \"legged robot\" OR \"robot dog\") AND (\"new\" OR \"update\" OR \"demo\") after:[today - 7d]\n```\n\n### 3.6 Mobile Manipulators\n```\n(\"mobile manipulator\" OR \"mobile manipulation\" OR \"wheeled robot arm\") after:[today - 7d]\n```\n\n### 3.7 Robot Compute & Edge AI Hardware\n```\n(\"robot compute\" OR \"edge AI chip\" OR \"onboard inference\") AND (\"robot\" OR \"NVIDIA Jetson\" OR \"Orin\") after:[today - 7d]\n```\n\n### 3.8 Robot Supply Chain & Manufacturing\n```\n(\"robot manufacturing\" OR \"robot supply chain\" OR \"robot production line\" OR \"robot component\") after:[today - 7d]\n```\n\n---\n\n## 4. Simulation & Infrastructure\n\n### 4.1 Simulation Platforms\n```\n(\"Isaac Sim\" OR \"Isaac Lab\" OR \"MuJoCo\" OR \"SAPIEN\" OR \"Genesis simulator\") AND (\"robot\" OR \"update\" OR \"release\") after:[today - 7d]\n```\n\n### 4.2 Digital Twins for Robotics\n```\n(\"digital twin\" OR \"synthetic data\") AND (\"robot\" OR \"factory\" OR \"warehouse\") after:[today - 7d]\n```\n\n### 4.3 Benchmarks & Evaluation\n```\n(\"robot benchmark\" OR \"manipulation benchmark\" OR \"SIMPLER\" OR \"RoboCasa\" OR \"ManiSkill\") after:[today - 7d]\n```\n\n### 4.4 Robot Datasets\n```\n(\"robot dataset\" OR \"Open X-Embodiment\" OR \"DROID dataset\" OR \"robot demonstration data\") after:[today - 30d]\n```\n\n### 4.5 Robot Operating System & Middleware\n```\n(\"ROS 2\" OR \"robot middleware\" OR \"robot software framework\") AND (\"update\" OR \"release\") after:[today - 30d]\n```\n\n---\n\n## 5. Deployments & Commercial\n\n### 5.1 Factory & Warehouse Deployments\n```\n(\"robot deployment\" OR \"robot factory\" OR \"warehouse robot\" OR \"logistics robot\") AND (\"humanoid\" OR \"embodied AI\") after:[today - 7d]\n```\n\n### 5.2 Task Success & Performance Metrics\n```\n(\"task success rate\" OR \"robot performance\" OR \"robot uptime\" OR \"pick rate\") AND (\"deployment\" OR \"production\") after:[today - 7d]\n```\n\n### 5.3 Household & Service Robots\n```\n(\"household robot\" OR \"home robot\" OR \"service robot\" OR \"domestic robot\") AND (\"AI\" OR \"embodied\") after:[today - 7d]\n```\n\n### 5.4 Healthcare & Medical Robotics\n```\n(\"surgical robot\" OR \"medical robot\" OR \"rehabilitation robot\") AND (\"AI\" OR \"learning\") after:[today - 7d]\n```\n\n### 5.5 Agriculture & Field Robotics\n```\n(\"agriculture robot\" OR \"farming robot\" OR \"field robot\" OR \"harvesting robot\") AND (\"AI\") after:[today - 7d]\n```\n\n### 5.6 Construction & Inspection Robots\n```\n(\"construction robot\" OR \"inspection robot\" OR \"infrastructure robot\") AND (\"AI\" OR \"autonomous\") after:[today - 7d]\n```\n\n---\n\n## 6. Funding, M&A & Business\n\n### 6.1 Robotics Funding Rounds\n```\n(\"robotics funding\" OR \"robot startup funding\" OR \"humanoid robot investment\") after:[today - 7d]\n```\n\n### 6.2 Embodied AI Startup News\n```\n(\"embodied AI startup\" OR \"robotics startup\" OR \"humanoid startup\") AND (\"funding\" OR \"launch\" OR \"raise\") after:[today - 7d]\n```\n\n### 6.3 M&A and Partnerships\n```\n(\"robotics acquisition\" OR \"robot company acquired\" OR \"robotics partnership\") after:[today - 7d]\n```\n\n### 6.4 IPO & Public Markets\n```\n(\"robotics IPO\" OR \"robot company public\" OR \"robotics SPAC\" OR \"robotics stock\") after:[today - 30d]\n```\n\n### 6.5 Market Sizing & Forecasts\n```\n(\"humanoid robot market\" OR \"robotics market size\" OR \"embodied AI TAM\") AND (\"forecast\" OR \"billion\" OR \"trillion\") after:[today - 30d]\n```\n\n---\n\n## 7. Policy, Safety & Ethics\n\n### 7.1 Robot Safety Standards\n```\n(\"robot safety\" OR \"robot safety standard\" OR \"ISO 10218\" OR \"ISO 13482\" OR \"collaborative robot safety\") after:[today - 30d]\n```\n\n### 7.2 AI & Robotics Regulation\n```\n(\"robot regulation\" OR \"AI regulation\" OR \"EU AI Act\") AND (\"robot\" OR \"embodied\" OR \"physical AI\") after:[today - 30d]\n```\n\n### 7.3 Export Controls & Geopolitics\n```\n(\"robot export control\" OR \"chip export\" OR \"robotics sanctions\") AND (\"China\" OR \"US\") after:[today - 30d]\n```\n\n### 7.4 Robot Ethics & Labor Impact\n```\n(\"robot ethics\" OR \"robot labor\" OR \"automation job displacement\" OR \"robot workforce\") after:[today - 30d]\n```\n\n---\n\n## 8. Company-Specific Queries\n\n### 🇺🇸 US Companies\n\n#### Tesla Optimus\n```\n(\"Tesla Optimus\" OR \"Tesla robot\" OR \"Tesla humanoid\" OR \"Tesla Bot\") after:[today - 7d]\n```\n\n#### Figure AI\n```\n(\"Figure AI\" OR \"Figure 02\" OR \"Figure humanoid\" OR \"Figure robot\") after:[today - 7d]\n```\n\n#### Boston Dynamics\n```\n(\"Boston Dynamics\" OR \"Atlas robot\" OR \"Spot robot\" OR \"Stretch robot\") after:[today - 7d]\n```\n\n#### Agility Robotics\n```\n(\"Agility Robotics\" OR \"Digit robot\" OR \"RoboFab\") after:[today - 7d]\n```\n\n#### 1X Technologies\n```\n(\"1X Technologies\" OR \"NEO robot\" OR \"1X humanoid\") after:[today - 7d]\n```\n\n#### Physical Intelligence (π)\n```\n(\"Physical Intelligence\" OR \"pi zero\" OR \"π0\" OR \"physical intelligence company\") AND (\"robot\") after:[today - 7d]\n```\n\n#### Skild AI\n```\n(\"Skild AI\" OR \"Skild robot\" OR \"Skild foundation model\") after:[today - 7d]\n```\n\n#### Apptronik\n```\n(\"Apptronik\" OR \"Apollo robot\" OR \"Apptronik humanoid\") after:[today - 7d]\n```\n\n#### Sanctuary AI\n```\n(\"Sanctuary AI\" OR \"Phoenix robot\" OR \"Carbon AI system\") after:[today - 7d]\n```\n\n#### Toyota Research Institute\n```\n(\"Toyota Research Institute\" OR \"TRI robot\" OR \"TRI manipulation\" OR \"TRI diffusion policy\") after:[today - 7d]\n```\n\n---\n\n### 🇨🇳 Chinese Companies & Labs\n\n#### Unitree (宇树)\n```\n(\"Unitree\" OR \"Unitree G1\" OR \"Unitree H1\" OR \"Unitree humanoid\" OR \"宇树\") after:[today - 7d]\n```\n\n#### AGIBOT / Zhiyuan (智元)\n```\n(\"AGIBOT\" OR \"Zhiyuan robot\" OR \"智元机器人\" OR \"AGIBOT humanoid\") after:[today - 7d]\n```\n\n#### UBTECH (优必选)\n```\n(\"UBTECH\" OR \"Walker robot\" OR \"优必选\" OR \"UBTECH humanoid\") after:[today - 7d]\n```\n\n#### Galbot (银河通用)\n```\n(\"Galbot\" OR \"银河通用\" OR \"Galbot robot\") after:[today - 7d]\n```\n\n#### Fourier Intelligence (傅利叶)\n```\n(\"Fourier Intelligence\" OR \"Fourier GR\" OR \"傅利叶\" OR \"Fourier humanoid\") after:[today - 7d]\n```\n\n#### Xiaomi CyberOne / Robotics\n```\n(\"Xiaomi robot\" OR \"CyberOne\" OR \"Xiaomi humanoid\" OR \"小米机器人\") after:[today - 7d]\n```\n\n#### Huawei / Peng Cheng Lab\n```\n(\"Huawei robot\" OR \"Peng Cheng Lab\" OR \"鹏城实验室\") AND (\"embodied\" OR \"robot\") after:[today - 7d]\n```\n\n#### Chinese Embodied AI Policy\n```\n(\"China humanoid robot\" OR \"China robot policy\" OR \"中国人形机器人\" OR \"具身智能政策\") after:[today - 30d]\n```\n\n---\n\n### 🌐 Platform & Infra Companies\n\n#### NVIDIA Robotics\n```\n(\"NVIDIA Isaac\" OR \"NVIDIA GR00T\" OR \"NVIDIA robot\" OR \"NVIDIA Isaac Lab\" OR \"NVIDIA Cosmos\") after:[today - 7d]\n```\n\n#### Google DeepMind Robotics\n```\n(\"DeepMind robot\" OR \"RT-2\" OR \"AutoRT\" OR \"Google robot\" OR \"DeepMind embodied\") after:[today - 7d]\n```\n\n#### Meta Robotics\n```\n(\"Meta robot\" OR \"Meta embodied\" OR \"Habitat simulator\" OR \"Meta AI robot\") after:[today - 7d]\n```\n\n#### Hugging Face Robotics\n```\n(\"LeRobot\" OR \"Hugging Face robot\" OR \"Hugging Face robotics\") after:[today - 7d]\n```\n\n---\n\n## 9. Academic & Research Queries\n\n### 9.1 arXiv Robotics\n```\nsite:arxiv.org (\"cs.RO\" OR \"robotics\") AND (\"embodied\" OR \"manipulation\" OR \"humanoid\" OR \"VLA\") after:[today - 7d]\n```\n\n### 9.2 arXiv — Vision-Language-Action\n```\nsite:arxiv.org (\"vision language action\" OR \"VLA\" OR \"visuomotor\") AND (\"robot\") after:[today - 7d]\n```\n\n### 9.3 arXiv — World Models for Robotics\n```\nsite:arxiv.org (\"world model\" OR \"video prediction\") AND (\"robot\" OR \"embodied\" OR \"manipulation\") after:[today - 7d]\n```\n\n### 9.4 Conference Papers\n```\n(\"CoRL 2026\" OR \"ICRA 2026\" OR \"RSS 2026\" OR \"IROS 2026\") AND (\"robot learning\" OR \"embodied\") after:[today - 30d]\n```\n\n### 9.5 Top Lab Publications\n```\n(\"Stanford\" OR \"CMU\" OR \"MIT\" OR \"Berkeley\" OR \"Tsinghua\" OR \"PKU\") AND (\"robot\" OR \"embodied AI\") AND (\"paper\" OR \"research\") after:[today - 7d]\n```\n\n### 9.6 Open-Source Robot Models & Code\n```\n(\"open source\" OR \"GitHub\") AND (\"robot model\" OR \"robot policy\" OR \"robot learning\" OR \"embodied AI\") after:[today - 7d]\n```\n\n---\n\n## 10. Source-Specific Queries\n\n### 10.1 Core Robotics Media\n```\nsite:therobotreport.com (\"humanoid\" OR \"embodied AI\" OR \"robot deployment\") after:[today - 1d]\n```\n```\nsite:spectrum.ieee.org (\"robot\" OR \"humanoid\" OR \"manipulation\") after:[today - 7d]\n```\n\n### 10.2 Tech Business Media\n```\nsite:techcrunch.com (\"robotics\" OR \"humanoid robot\" OR \"embodied AI\") after:[today - 7d]\n```\n```\nsite:reuters.com (\"humanoid robot\" OR \"robotics\" OR \"robot factory\") after:[today - 7d]\n```\n\n### 10.3 Company Blogs\n```\nsite:developer.nvidia.com/blog (\"robotics\" OR \"Isaac\" OR \"GR00T\") after:[today - 30d]\n```\n```\nsite:deepmind.google (\"robot\" OR \"embodied\" OR \"manipulation\") after:[today - 30d]\n```\n```\nsite:bostondynamics.com/blog after:[today - 30d]\n```\n\n### 10.4 Chinese Media (English Coverage)\n```\nsite:syncedreview.com (\"robot\" OR \"embodied\" OR \"humanoid\") after:[today - 7d]\n```\n```\n(\"China robot\" OR \"Chinese humanoid\") AND (\"news\" OR \"launch\" OR \"funding\") after:[today - 7d]\n```\n\n### 10.5 GitHub — Embodied AI Repository Discovery\n\n> Use with **`github_repos.md`** for filtering, ranking, and output schema. Prefer fetching the linked GitHub page to verify description, archived status, and star count.\n\n#### 10.5.1 GitHub search — high stars (popularity proxy)\n```\nsite:github.com (\"robot\" OR \"humanoid\" OR \"manipulation\" OR \"VLA\" OR \"diffusion policy\" OR \"imitation learning\" OR \"sim2real\" OR \"embodied\") stars:>500\n```\n\n#### 10.5.2 Vision-language-action & generalist policies\n```\nsite:github.com (\"vision-language-action\" OR \"VLA\" OR \"robot policy\" OR \"lerobot\" OR \"openvla\") stars:>100\n```\n\n#### 10.5.3 Simulation stacks & benchmarks\n```\nsite:github.com (\"Isaac Lab\" OR \"IsaacGym\" OR \"mujoco\" OR \"robosuite\" OR \"mani_skill\" OR \"habitat-lab\" OR \"ORBIT\") stars:>50\n```\n\n#### 10.5.4 Data, teleoperation, datasets\n```\nsite:github.com (\"robot dataset\" OR \"teleoperation\" OR \"robot learning dataset\" OR \"dexterous manipulation\") stars:>50\n```\n\n#### 10.5.5 Recent activity (weak “trending” proxy via recency)\n```\nsite:github.com (\"embodied AI\" OR \"robot learning\" OR \"humanoid\") pushed:>2026-01-01\n```\n(Adjust the date seasonally toward `[today - 90d]` when the year rolls forward.)\n\n#### 10.5.6 Chinese ecosystem repos (bilingual keywords)\n```\nsite:github.com (\"人形机器人\" OR \"具身智能\" OR \"quadruped\" OR \"Unitree\" OR \"mujoco\") stars:>30\n```\n\n---\n\n## 11. Query Combination Recipes\n\n### 📰 Recipe A: Daily Briefing (5 queries, ~15 min)\n```\nQ1: (\"embodied AI\" OR \"humanoid robot\") AND (\"news\" OR \"announcement\") after:[today - 1d]\nQ2: (\"robot foundation model\" OR \"VLA\" OR \"diffusion policy\") AND (\"new\" OR \"paper\") after:[today - 1d]\nQ3: (\"Tesla Optimus\" OR \"Figure AI\" OR \"Boston Dynamics\" OR \"Unitree\") after:[today - 1d]\nQ4: (\"robotics funding\" OR \"robot startup\") AND (\"raise\" OR \"funding\") after:[today - 7d]\nQ5: site:therobotreport.com OR site:spectrum.ieee.org (\"robot\") after:[today - 1d]\n```\n\n### 🔬 Recipe B: Weekly Research Deep Dive (4 queries, ~30 min)\n```\nQ1: site:arxiv.org (\"cs.RO\") AND (\"embodied\" OR \"VLA\" OR \"manipulation\" OR \"humanoid\") after:[today - 7d]\nQ2: (\"diffusion policy\" OR \"world model\" OR \"imitation learning\") AND (\"robot\") after:[today - 7d]\nQ3: (\"sim-to-real\" OR \"sim2real\" OR \"cross-embodiment\" OR \"generalist policy\") after:[today - 7d]\nQ4: (\"open source\" OR \"GitHub\") AND (\"robot model\" OR \"robot policy\") after:[today - 7d]\n```\n\n### 🏭 Recipe C: Commercial & Deployment Tracker (4 queries, ~20 min)\n```\nQ1: (\"robot deployment\" OR \"robot factory\" OR \"warehouse robot\") AND (\"humanoid\" OR \"embodied\") after:[today - 7d]\nQ2: (\"robotics funding\" OR \"robotics acquisition\" OR \"robotics IPO\") after:[today - 7d]\nQ3: (\"humanoid robot market\" OR \"robotics market\") AND (\"forecast\" OR \"billion\") after:[today - 30d]\nQ4: (\"robot supply chain\" OR \"actuator\" OR \"dexterous hand\") AND (\"new\" OR \"production\") after:[today - 7d]\n```\n\n### 🇨🇳 Recipe D: China Ecosystem Focus (4 queries, ~15 min)\n```\nQ1: (\"Unitree\" OR \"AGIBOT\" OR \"UBTECH\" OR \"Galbot\" OR \"Fourier\") after:[today - 7d]\nQ2: (\"China humanoid robot\" OR \"China robot policy\" OR \"China embodied AI\") after:[today - 7d]\nQ3: site:syncedreview.com (\"robot\" OR \"embodied\") after:[today - 7d]\nQ4: (\"中国人形机器人\" OR \"具身智能\" OR \"机器人政策\") after:[today - 7d]\n```\n\n### 🦾 Recipe E: Hardware & Supply Chain (4 queries, ~20 min)\n```\nQ1: (\"humanoid robot\" OR \"bipedal robot\") AND (\"new\" OR \"launch\" OR \"specs\" OR \"demo\") after:[today - 7d]\nQ2: (\"dexterous hand\" OR \"robot hand\" OR \"tactile sensor\") AND (\"new\" OR \"breakthrough\") after:[today - 7d]\nQ3: (\"robot actuator\" OR \"harmonic drive\" OR \"quasi-direct-drive\") after:[today - 30d]\nQ4: (\"NVIDIA Jetson\" OR \"edge AI\" OR \"onboard compute\") AND (\"robot\") after:[today - 7d]\n```\n\n### ⭐ Recipe F: GitHub Hot Repos — Embodied AI (4–6 queries, ~10–15 min)\n\n**Goal**: Shortlist **5–8** canonical repos for the **⭐ GitHub 热门开源** section.\n\n```\nF1: site:github.com (\"VLA\" OR \"vision language action\" OR \"diffusion policy\" OR \"robot policy\") stars:>200\nF2: site:github.com (\"Isaac Lab\" OR \"Isaac Sim\" OR \"mujoco\" OR \"robosuite\" OR \"habitat-lab\") stars:>100\nF3: site:github.com (\"lerobot\" OR \"openvla\" OR \"octo model\" OR \"RT-X\" OR \"cross-embodiment\") stars:>50\nF4: site:github.com (\"sim2real\" OR \"sim-to-real\" OR \"domain randomization\") AND (\"robot\") stars:>100\nF5: site:github.com (\"humanoid\" OR \"quadruped\" OR \"whole body\") AND (\"reinforcement learning\" OR \"learning\") stars:>100\nF6 (optional): site:github.com (\"具身智能\" OR \"人形机器人\" OR \"robot learning\") stars:>50\n```\n\nAfter search: apply **`github_repos.md` → Relevance Filter & Rank**; verify each repo URL; do not report unverified star counts.\n\n---\n\n## 12. Query Optimization Tips for Embodied AI\n\n### Terminology Precision\n| ❌ Too Broad | ✅ Precise |\n|-------------|-----------|\n| `\"robot news\"` | `\"humanoid robot\" OR \"embodied AI\"` |\n| `\"AI model\"` | `\"VLA model\" OR \"diffusion policy\" OR \"robot foundation model\"` |\n| `\"robot arm\"` | `\"dexterous manipulation\" OR \"mobile manipulator\"` |\n| `\"simulation\"` | `\"Isaac Sim\" OR \"MuJoCo\" OR \"sim-to-real\"` |\n\n### Noise Exclusion Patterns\n```\n# Exclude industrial-only / non-AI robotics\nNOT \"Roomba\" NOT \"RPA\" NOT \"software robot\" NOT \"chatbot\" NOT \"trading bot\"\n\n# Exclude adjacent but different fields\nNOT \"autonomous vehicle\" NOT \"self-driving\" NOT \"drone delivery\"\n(unless specifically tracking these intersections)\n```\n\n### Chinese Content Search Tips\n- Use **both English and Chinese** terms for Chinese companies: `\"Unitree\" OR \"宇树\"`\n- For policy: `\"具身智能\" OR \"人形机器人\" OR \"embodied AI China\"`\n- Best Chinese search engines: **Baidu News**, **WeChat Search (搜狗微信)**\n- For English coverage of China: `site:syncedreview.com` or `\"China\" AND \"humanoid robot\"`\n\n### Date Range Guidelines\n| Content Type | Recommended Range | Rationale |\n|-------------|-------------------|-----------|\n| Breaking news & demos | `after:[today - 1d]` | Fast-moving announcements |\n| Research papers | `after:[today - 7d]` | Papers accumulate weekly |\n| Funding rounds | `after:[today - 7d]` | Deal flow is weekly cadence |\n| Hardware launches | `after:[today - 7d]` | Product cycles are slower |\n| Policy & regulation | `after:[today - 30d]` | Policy moves slowly |\n| Market reports | `after:[today - 30d]` | Published monthly/quarterly |\n| Supply chain | `after:[today - 30d]` | Long-cycle industry |\n\nFile v1.0.5:references/taxonomy.md\n\n# 📊 Embodied AI — Taxonomy & Glossary\n\nA unified classification system and keyword dictionary for the Embodied AI news tracking system.\nEnsures consistent categorization across `search_queries.md`, `output_templates.md`, and `workflow.md`.\n\n---\n\n## How to Use This File\n\n| Who | How |\n|-----|-----|\n| **Search** (`search_queries.md`) | Use the keyword lists to build/refine queries; use aliases to avoid missing results |\n| **Classification** (`workflow.md` Step 3) | Use the category tree to assign each story to the correct bucket |\n| **Output** (`output_templates.md`) | Use the metadata fields per category to fill template fields consistently |\n| **Monthly Maintenance** | Add new terms, retire obsolete ones, re-classify as the field evolves |\n\n---\n\n## 1. News Category Taxonomy\n\nThe primary classification system for all news stories. Every story must be assigned to **exactly one** primary category and may have **0–2** secondary tags.\n\n```\n📰 Embodied AI News\n│\n├── 🔥 Major Announcements          ← Cross-cutting; reserved for top-impact stories\n│\n├── 🧠 Foundation Models & Algorithms\n│   ├── Vision-Language-Action (VLA) Models\n│   ├── Diffusion / Flow-based Policies\n│   ├── World Models\n│   ├── Reinforcement Learning\n│   ├── Imitation Learning & Teleoperation\n│   ├── Sim-to-Real Transfer\n│   ├── Language-Conditioned Robotics\n│   ├── 3D / Spatial Understanding\n│   ├── Generalist / Cross-Embodiment Policies\n│   └── Multimodal Perception (Vision, Tactile, Audio)\n│\n├── 🦾 Hardware & Platforms\n│   ├── Humanoid Robots (Full-body Bipedal)\n│   ├── Quadruped / Legged Robots\n│   ├── Mobile Manipulators\n│   ├── Dexterous Hands & Grippers\n│   ├── Actuators & Transmission\n│   ├── Sensors (Tactile, Force-Torque, Vision)\n│   ├── Compute & Edge AI Hardware\n│   └── Supply Chain & Manufacturing\n│\n├── 🌐 Simulation & Infrastructure\n│   ├── Simulation Platforms\n│   ├── Digital Twins\n│   ├── Benchmarks & Evaluation\n│   ├── Datasets\n│   ├── Robot OS & Middleware\n│   └── Cloud Robotics & Fleet Management\n│\n├── 🏭 Deployments & Commercial\n│   ├── Factory & Warehouse\n│   ├── Household & Service\n│   ├── Healthcare & Medical\n│   ├── Agriculture & Field\n│   ├── Construction & Inspection\n│   ├── Retail & Hospitality\n│   └── Performance Metrics & Benchmarks\n│\n├── 💰 Funding, M&A & Business\n│   ├── Funding Rounds\n│   ├── M&A & Partnerships\n│   ├── IPO & Public Markets\n│   ├── Market Sizing & Forecasts\n│   └── Talent & Hiring\n│\n├── 🌍 Policy, Safety & Ethics\n│   ├── Safety Standards\n│   ├── Government Regulation\n│   ├── Export Controls & Geopolitics\n│   ├── Ethics & Labor Impact\n│   └── Industry Consortia & Standards Bodies\n│\n└── 🇨🇳 China Ecosystem\n    ├── Company News (Unitree, AGIBOT, UBTECH, Galbot, Fourier, etc.)\n    ├── Policy & Subsidies\n    ├── Supply Chain & Manufacturing\n    ├── Academic & Research\n    └── Market & Competition\n```\n\n### Category Assignment Rules\n\n| Rule | Description |\n|------|-------------|\n| **Single Primary** | Every story gets exactly one primary category |\n| **Major Announcements** | Only for stories that would be \"above the fold\" — new paradigm, >$500M funding, first-ever deployment milestone, etc. Also assign a secondary category |\n| **China Ecosystem** | Use when the story's primary significance is about the Chinese market/ecosystem. If a Chinese company publishes a technical paper, primary = 🧠, secondary = 🇨🇳 |\n| **Cross-cutting stories** | A story about \"Unitree raises $500M to scale humanoid production\" → Primary: 💰, Secondary: 🇨🇳, 🦾 |\n| **When in doubt** | Ask: \"What is the reader most interested in learning from this story?\" — that determines the primary category |\n\n---\n\n## 2. Technology Taxonomy\n\n### 2.1 Learning Paradigms\n\n```\nLearning Paradigms\n│\n├── Imitation Learning (IL)\n│   ├── Behavioral Cloning (BC)\n│   ├── Inverse Reinforcement Learning (IRL)\n│   ├── DAgger / Interactive IL\n│   └── One-Shot / Few-Shot IL\n│\n├── Reinforcement Learning (RL)\n│   ├── Model-Free RL (PPO, SAC, TD3)\n│   ├── Model-Based RL\n│   ├── Offline RL / Batch RL\n│   ├── Sim-to-Real RL\n│   ├── Reward Shaping / Reward Learning\n│   └── Curriculum Learning\n│\n├── Foundation Model Approaches\n│   ├── Vision-Language-Action (VLA)\n│   ├── Vision-Language Models for Planning (VLM)\n│   ├── Large Language Model Planning (LLM-as-Planner)\n│   ├── World Models / Video Prediction\n│   ├── Diffusion Policy\n│   ├── Flow Matching Policy\n│   ├── Action Chunking (ACT)\n│   ├── Generalist Policy / Cross-Embodiment\n│   └── Large Behavior Models (LBM)\n│\n├── Sim-to-Real Transfer\n│   ├── Domain Randomization\n│   ├── Domain Adaptation\n│   ├── System Identification\n│   ├── Real-to-Sim-to-Real\n│   └── Digital Twin Transfer\n│\n└── Data Collection & Curation\n    ├── Teleoperation (VR, Exoskeleton, Puppet)\n    ├── Human Video Demonstration\n    ├── Synthetic Data Generation\n    ├── Autonomous Data Collection (AutoRT-style)\n    ├── Cross-Embodiment Datasets\n    └── Data Scaling Laws\n```\n\n### 2.2 Model Architecture Taxonomy\n\n```\nModel Architectures\n│\n├── Vision-Language-Action (VLA)\n│   ├── RT-2 / RT-2-X (Google DeepMind)\n│   ├── Octo (Berkeley)\n│   ├── OpenVLA (Stanford/Berkeley)\n│   ├── π0 / π0-FAST (Physical Intelligence)\n│   ├── GR00T (NVIDIA)\n│   ├── RoboVLM\n│   ├── SpatialVLA\n│   └── [Emerging: company-specific VLAs]\n│\n├── Diffusion-Based Policies\n│   ├── Diffusion Policy (Chi et al.)\n│   ├── 3D Diffusion Policy (DP3)\n│   ├── Consistency Policy\n│   └── Flow Matching Policy\n│\n├── Action Chunking\n│   ├── ACT (Action Chunking with Transformers)\n│   └── ACT variants (BiACT, etc.)\n│\n├── World Models\n│   ├── Video Prediction Models (UniSim, Cosmos, etc.)\n│   ├── Latent World Models\n│   ├── Physics-Informed World Models\n│   └── Action-Conditioned Video Generation\n│\n├── LLM / VLM Planners\n│   ├── SayCan / Inner Monologue\n│   ├── Code-as-Policy\n│   ├── VoxPoser\n│   └── Task and Motion Planning (TAMP) + LLM\n│\n└── Classical / Hybrid\n    ├── Model Predictive Control (MPC)\n    ├── Whole-Body Control (WBC)\n    ├── Trajectory Optimization\n    └── Hybrid Learning + Control\n```\n\n### 2.3 Perception Stack\n\n```\nPerception\n│\n├── Visual Perception\n│   ├── RGB Camera (Monocular, Stereo)\n│   ├── Depth Sensors (Structured Light, ToF, LiDAR)\n│   ├── Object Detection & Segmentation\n│   ├── 6D Pose Estimation\n│   ├── Open-Vocabulary Detection (OWL-ViT, Grounding DINO)\n│   └── Visual Foundation Models (DINOv2, SAM, etc.)\n│\n├── 3D / Spatial Perception\n│   ├── Point Cloud Processing\n│   ├── NeRF / 3D Gaussian Splatting\n│   ├── Occupancy Networks\n│   ├── Scene Graphs\n│   └── Spatial Reasoning\n│\n├── Tactile Perception\n│   ├── Vision-Based Tactile (GelSight, DIGIT, Taxim)\n│   ├── Capacitive / Resistive Arrays\n│   ├── Tactile-Visual Fusion\n│   └── Slip Detection\n│\n├── Proprioception\n│   ├── Joint Encoders\n│   ├── IMU / Inertial Measurement\n│   ├── Force-Torque Sensors\n│   └── Current-Based Torque Estimation\n│\n└── Multimodal Fusion\n    ├── Vision-Language Grounding\n    ├── Vision-Tactile Fusion\n    ├── Audio-Visual Fusion\n    └── Cross-Modal Representation\n```\n\n---\n\n## 3. Hardware Taxonomy\n\n### 3.1 Robot Form Factors\n\n```\nRobot Form Factors\n│\n├── Humanoid (Bipedal, Full-Body)\n│   ├── Full-Size (>150cm): Atlas, Optimus, Figure 02, Walker S\n│   ├── Mid-Size (100–150cm): GR-2, H1, NEO, Phoenix\n│   ├── Compact (<100cm): G1, GR-1\n│   └── Upper-Body Only (Torso + Arms): ALOHA, Mobile ALOHA\n│\n├── Quadruped / Legged\n│   ├── Spot (Boston Dynamics)\n│   ├── Go2 / B2 (Unitree)\n│   ├── ANYmal (ANYbotics)\n│   └── Custom Research Platforms\n│\n├── Mobile Manipulator\n│   ├── Wheeled Base + Arm(s)\n│   ├── Stretch (Hello Robot)\n│   ├── TIAGo (PAL Robotics)\n│   └── Custom Lab Platforms\n│\n├── Tabletop / Fixed-Base Arm\n│   ├── Franka Emika (Panda)\n│   ├── Universal Robots (UR series)\n│   ├── xArm / Flexiv Rizon\n│   ├── ALOHA (Bimanual)\n│   └── Low-Cost Arms (Koch, SO-100, Gello, etc.)\n│\n└── Specialized\n    ├── Surgical Robots\n    ├── Agricultural Robots\n    ├── Underwater Robots\n    └── Aerial Manipulators\n```\n\n### 3.2 Key Components\n\n```\nKey Components\n│\n├── Actuators & Transmission\n│   ├── Harmonic Drive / Strain Wave\n│   ├── Planetary Gearbox\n│   ├── Quasi-Direct-Drive (QDD)\n│   ├── Linear Actuators\n│   ├── Series Elastic Actuators (SEA)\n│   ├── Hydraulic Actuators\n│   ├── Tendon-Driven Mechanisms\n│   ├── BLDC Motors\n│   └── Frameless Motors\n│\n├── Dexterous Hands\n│   ├── Anthropomorphic (5-finger)\n│   │   ├── Shadow Hand\n│   │   ├── Ability Hand (PSYONIC)\n│   │   ├── Inspire Hand\n│   │   ├── Leap Hand\n│   │   └── Company-specific (Figure, Tesla, AGIBOT, etc.)\n│   ├── Under-Actuated Grippers\n│   ├── Soft Grippers\n│   └── Parallel Jaw Grippers\n│\n├── Sensors\n│   ├── Cameras (RGB, Depth, Event)\n│   ├── LiDAR\n│   ├── Tactile Sensors\n│   ├── Force-Torque Sensors (F/T)\n│   ├── IMU\n│   ├── Joint Encoders (Absolute, Incremental)\n│   └── Proximity Sensors\n│\n├── Compute Platforms\n│   ├── NVIDIA Jetson (Orin, Thor)\n│   ├── Qualcomm Robotics RB series\n│   ├── Intel / AMD Embedded\n│   ├── Custom ASICs\n│   └── Cloud Offloading\n│\n└── Power Systems\n    ├── Battery (LiFePO4, Li-ion)\n    ├── Battery Management System (BMS)\n    └── Power Distribution\n```\n\n---\n\n## 4. Company & Organization Taxonomy\n\n### 4.1 Humanoid Robot Companies\n\n| Company | HQ | Latest Robot | Key Tech | Aliases & Search Terms |\n|---------|-----|-------------|----------|----------------------|\n| **Tesla** | 🇺🇸 Austin, TX | Optimus Gen 3 | End-to-end NN, FSD transfer | `Tesla Optimus`, `Tesla Bot`, `Tesla humanoid` |\n| **Figure AI** | 🇺🇸 Sunnyvale, CA | Figure 02 | VLA + LLM (OpenAI collab) | `Figure AI`, `Figure 02`, `Figure robot` |\n| **Boston Dynamics** | 🇺🇸 Waltham, MA | Electric Atlas | Whole-body athletic | `Boston Dynamics`, `Atlas`, `Electric Atlas` |\n| **Agility Robotics** | 🇺🇸 Corvallis, OR | Digit | Warehouse logistics | `Agility Robotics`, `Digit`, `RoboFab` |\n| **1X Technologies** | 🇳🇴 Moss, Norway | NEO Gamma | Tendon-driven, embodied AI | `1X Technologies`, `NEO`, `1X robot` |\n| **Apptronik** | 🇺🇸 Austin, TX | Apollo | Modular, Mercedes collab | `Apptronik`, `Apollo robot` |\n| **Sanctuary AI** | 🇨🇦 Vancouver, BC | Phoenix | Carbon AI control system | `Sanctuary AI`, `Phoenix robot`, `Carbon` |\n| **Physical Intelligence** | 🇺🇸 San Francisco, CA | — (software) | π0, π0-FAST | `Physical Intelligence`, `pi zero`, `π0` |\n| **Skild AI** | 🇺🇸 Pittsburgh, PA | — (software) | Scalable robot foundation model | `Skild AI`, `Skild robot` |\n| **Unitree** | 🇨🇳 Hangzhou | G1, H1, B2-W | Low-cost, mass production | `Unitree`, `宇树`, `Unitree G1`, `Unitree H1` |\n| **AGIBOT (Zhiyuan)** | 🇨🇳 Shanghai | A2, GENIE | Full-stack, VLA model | `AGIBOT`, `智元`, `智元机器人`, `Zhiyuan` |\n| **UBTECH** | 🇨🇳 Shenzhen | Walker S2 | Public company, factory deploy | `UBTECH`, `优必选`, `Walker S` |\n| **Galbot** | 🇨🇳 Shanghai | Galbot G1 | Mobile manipulation | `Galbot`, `银河通用`, `银河通用机器人` |\n| **Fourier Intelligence** | 🇨🇳 Shanghai | GR-2 | Rehab origin, open platform | `Fourier Intelligence`, `傅利叶`, `Fourier GR` |\n| **Xiaomi** | 🇨🇳 Beijing | CyberOne 2 | Consumer electronics crossover | `Xiaomi robot`, `CyberOne`, `小米机器人` |\n| **XPeng Robotics** | 🇨🇳 Guangzhou | Iron | Auto industry crossover | `XPeng robot`, `小鹏机器人`, `Iron robot` |\n| **Kepler** | 🇨🇳 Shanghai | Forerunner K2 | Industrial focus | `Kepler robot`, `开普勒`, `Forerunner` |\n| **Robot Era** | 🇨🇳 Beijing | STAR1 | Agile locomotion | `Robot Era`, `星动纪元`, `STAR1` |\n| **Booster Robotics** | 🇨🇳 Shenzhen | Booster T1 | Lightweight bipedal | `Booster Robotics`, `加速进化` |\n| **LimX Dynamics** | 🇨🇳 Shenzhen | CL-2 | Legged locomotion | `LimX Dynamics`, `逐际动力` |\n| **Noetix** | 🇨🇳 Beijing | N1 | Tsinghua spin-off | `Noetix`, `星海图` |\n\n### 4.2 Platform & Infrastructure Companies\n\n| Company | Role | Key Products | Search Terms |\n|---------|------|-------------|-------------|\n| **NVIDIA** | GPU + Sim + Foundation Model | Isaac Sim/Lab, GR00T, Cosmos, Jetson | `NVIDIA Isaac`, `NVIDIA GR00T`, `NVIDIA Cosmos` |\n| **Google DeepMind** | Research + Models | RT-2, AutoRT, Gemini Robotics | `DeepMind robot`, `RT-2`, `Gemini Robotics` |\n| **Meta FAIR** | Research + Open Source | Habitat, embodied research | `Meta robot`, `Habitat`, `Meta embodied` |\n| **Hugging Face** | Open-Source Hub | LeRobot, model hosting | `LeRobot`, `Hugging Face robot` |\n| **Toyota Research (TRI)** | Research + Demos | Diffusion Policy, LBM | `TRI robot`, `Toyota Research Institute` |\n| **Amazon / Lab126** | Deployment + Research | Warehouse robotics | `Amazon robot`, `Sparrow`, `Lab126` |\n\n### 4.3 Academic Labs (Tier 1)\n\n| Lab | Affiliation | Focus Areas | Key People |\n|-----|------------|-------------|-----------|\n| **IRIS Lab** | Stanford | VLA, Diffusion Policy, ALOHA | Chelsea Finn, Sergey Levine (adj.) |\n| **RAIL** | UC Berkeley | Robot learning, open-source models | Sergey Levine, Pieter Abbeel |\n| **Robotic Exploration Lab** | CMU | Locomotion, manipulation | Deepak Pathak |\n| **CSAIL** | MIT | Manipulation, soft robotics | Pulkit Agrawal, Russ Tedrake |\n| **PAIR Lab** | Tsinghua | Embodied AI, humanoid | Hao Dong (董豪) |\n| **IIIS** | Tsinghua | Robot learning | Yi Wu, Huazhe Xu |\n| **CFCS** | PKU | Embodied intelligence | He Wang, Hao Su |\n| **CLOVER Lab** | Shanghai AI Lab | VLA, embodied foundation model | — |\n| **Robotics @ DeepMind** | Google DeepMind | RT-X, AutoRT, Gemini Robotics | Kanishka Rao |\n| **TRI Robotics** | Toyota | Diffusion Policy, LBM, dexterous | Russ Tedrake, Ben Burchfiel |\n\n---\n\n## 5. Application Domain Taxonomy\n\n```\nApplication Domains\n│\n├── Industrial / Manufacturing\n│   ├── Assembly Line (pick-and-place, screw driving, insertion)\n│   ├── Quality Inspection\n│   ├── Material Handling\n│   ├── Packaging & Palletizing\n│   └── Machine Tending\n│\n├── Logistics & Warehouse\n│   ├── Order Picking\n│   ├── Sorting\n│   ├── Goods-to-Person\n│   ├── Last-Mile Delivery\n│   └── Inventory Management\n│\n├── Household & Consumer\n│   ├── Tidying / Cleaning\n│   ├── Kitchen / Cooking\n│   ├── Laundry (Folding, Sorting)\n│   ├── Elderly Care / Assistance\n│   └── Entertainment / Companionship\n│\n├── Healthcare & Medical\n│   ├── Surgical Assistance\n│   ├── Rehabilitation\n│   ├── Hospital Logistics\n│   ├── Nursing Assistance\n│   └── Lab Automation\n│\n├── Agriculture & Food\n│   ├── Harvesting\n│   ├── Weeding / Spraying\n│   ├── Livestock Management\n│   └── Food Processing\n│\n├── Construction & Infrastructure\n│   ├── Bricklaying / 3D Printing\n│   ├── Welding / Cutting\n│   ├── Inspection (Bridge, Pipeline, Power Line)\n│   └── Demolition\n│\n├── Retail & Hospitality\n│   ├── Shelf Stocking\n│   ├── Customer Service\n│   ├── Food Service / Delivery\n│   └── Hotel Service\n│\n└── Research & Education\n    ├── Lab Research Platform\n    ├── STEM Education\n    └── Competition (RoboCup, DARPA)\n```\n\n---\n\n## 6. Simulation & Benchmark Taxonomy\n\n### 6.1 Simulation Platforms\n\n| Platform | Developer | Physics Engine | Key Strengths | Search Terms |\n|----------|-----------|---------------|---------------|-------------|\n| **Isaac Sim / Isaac Lab** | NVIDIA | PhysX 5 | GPU-parallel, photorealistic | `Isaac Sim`, `Isaac Lab`, `Isaac Gym` |\n| **MuJoCo** | Google DeepMind | MuJoCo | Fast contact, research standard | `MuJoCo` |\n| **SAPIEN** | UC San Diego / Hillbot | PhysX 5 | Articulated objects, ManiSkill | `SAPIEN`, `ManiSkill` |\n| **Genesis** | Genesis Team | Custom | Differentiable, fast | `Genesis simulator` |\n| **PyBullet** | Erwin Coumans | Bullet | Lightweight, open-source | `PyBullet` |\n| **Gazebo** | Open Robotics | ODE/Bullet/DART | ROS integration | `Gazebo` |\n| **Habitat** | Meta | Custom | Navigation, embodied QA | `Habitat simulator` |\n| **RoboCasa** | UT Austin | MuJoCo | Household tasks | `RoboCasa` |\n| **LIBERO** | UT Austin | MuJoCo | Lifelong learning benchmark | `LIBERO benchmark` |\n| **RLBench** | Stephen James | CoppeliaSim | 100 manipulation tasks | `RLBench` |\n| **Calvin** | Uni Freiburg | PyBullet | Language-conditioned | `CALVIN benchmark` |\n\n### 6.2 Datasets\n\n| Dataset | Scale | Content | Search Terms |\n|---------|-------|---------|-------------|\n| **Open X-Embodiment** | 1M+ episodes, 22 robots | Cross-embodiment manipulation | `Open X-Embodiment`, `OXE` |\n| **DROID** | 76K episodes | Bimanual manipulation, diverse | `DROID dataset` |\n| **BridgeData V2** | 60K+ trajectories | Tabletop manipulation | `BridgeData` |\n| **RH20T** | 110K+ episodes | Chinese lab, diverse tasks | `RH20T` |\n| **RoboSet** | 100K+ trajectories | Multi-skill manipulation | `RoboSet` |\n| **ALOHA Datasets** | Various | Bimanual fine manipulation | `ALOHA dataset` |\n| **RoboMIND** | 55K+ episodes | AGIBOT, real-world | `RoboMIND` |\n\n---\n\n## 7. Conference & Venue Taxonomy\n\n### 7.1 Tier 1 — Must Track\n\n| Conference | Full Name | Typical Date | Focus |\n|-----------|-----------|-------------|-------|\n| **CoRL** | Conference on Robot Learning | Oct–Nov | Robot learning (core venue) |\n| **ICRA** | IEEE Intl. Conf. on Robotics & Automation | May–Jun | Broad robotics |\n| **RSS** | Robotics: Science and Systems | Jul | Top-tier robotics theory |\n| **IROS** | IEEE/RSJ Intl. Conf. on Intelligent Robots & Systems | Oct | Broad robotics |\n| **NeurIPS** | Neural Information Processing Systems | Dec | ML (robot learning track) |\n| **ICML** | Intl. Conf. on Machine Learning | Jul | ML (robot learning track) |\n| **ICLR** | Intl. Conf. on Learning Representations | Apr–May | ML (growing robot track) |\n| **CVPR** | Computer Vision and Pattern Recognition | Jun | Vision (embodied track) |\n\n### 7.2 Tier 2 — Important\n\n| Conference | Focus |\n|-----------|-------|\n| **HRI** | Human-Robot Interaction |\n| **WAFR** | Algorithmic Foundations of Robotics |\n| **Humanoids** | IEEE-RAS Intl. Conf. on Humanoid Robots |\n| **RoboCup** | Robot competition |\n| **ACL** | NLP (language grounding for robots) |\n| **ECCV / ICCV** | Vision (embodied perception) |\n\n### 7.3 Industry Events\n\n| Event | Typical Date | Why It Matters |\n|-------|-------------|---------------|\n| **CES** | Jan | Consumer robot reveals |\n| **NVIDIA GTC** | Mar | Isaac / GR00T announcements |\n| **Google I/O** | May | DeepMind robotics demos |\n| **Automate** | May | Industrial robotics trade show |\n| **WRC (World Robot Conference)** | Aug | China ecosystem showcase |\n| **CIFTIS** | Sep | China service trade (robot demos) |\n\n---\n\n## 8. Keyword Dictionary\n\nA comprehensive bilingual (EN/CN) keyword list for search and classification.\n\n### 8.1 Core Concepts\n\n| English | Chinese | Aliases / Variants |\n|---------|---------|-------------------|\n| Embodied AI | 具身智能 | Embodied Intelligence, Physical AI |\n| Humanoid Robot | 人形机器人 | Bipedal Robot, Android |\n| Foundation Model | 基础模型 / 大模型 | Base Model, Pretrained Model |\n| Vision-Language-Action | 视觉-语言-动作 | VLA |\n| Diffusion Policy | 扩散策略 | Action Diffusion |\n| World Model | 世界模型 | Predictive Model, Video Prediction Model |\n| Imitation Learning | 模仿学习 | Learning from Demonstration, LfD |\n| Reinforcement Learning | 强化学习 | RL |\n| Sim-to-Real | 仿真到真实 | Sim2Real, Simulation Transfer |\n| Teleoperation | 遥操作 | Remote Control, Puppet Control |\n| Dexterous Manipulation | 灵巧操作 | In-Hand Manipulation, Fine Manipulation |\n| Locomotion | 运动控制 | Walking, Bipedal Locomotion |\n| Whole-Body Control | 全身控制 | WBC |\n| Grasping | 抓取 | Grasp Planning |\n| Mobile Manipulation | 移动操作 | Navigate-and-Manipulate |\n| Cross-Embodiment | 跨本体 | Multi-Robot, Embodiment-Agnostic |\n| Generalist Policy | 通用策略 | General-Purpose Policy |\n| Large Behavior Model | 大行为模型 | LBM |\n| Action Chunking | 动作分块 | ACT |\n| Open Vocabulary | 开放词汇 | Zero-Shot Detection |\n\n### 8.2 Hardware Terms\n\n| English | Chinese | Aliases / Variants |\n|---------|---------|-------------------|\n| Actuator | 执行器 / 驱动器 | Motor, Drive |\n| Harmonic Drive | 谐波减速器 | Strain Wave Gear |\n| Planetary Gearbox | 行星减速器 | Planetary Reducer |\n| Quasi-Direct-Drive | 准直驱 | QDD |\n| BLDC Motor | 无刷直流电机 | Brushless DC Motor |\n| Dexterous Hand | 灵巧手 | Robot Hand, Anthropomorphic Hand |\n| Tactile Sensor | 触觉传感器 | Tactile Array |\n| Force-Torque Sensor | 力矩传感器 | F/T Sensor, 6-axis F/T |\n| End Effector | 末端执行器 | Gripper, Tool |\n| Degrees of Freedom | 自由度 | DoF |\n| Payload | 负载 | Load Capacity |\n| Battery Life | 续航 | Runtime |\n| Edge Computing | 边缘计算 | Onboard Compute |\n\n### 8.3 Deployment & Business Terms\n\n| English | Chinese | Aliases / Variants |\n|---------|---------|-------------------|\n| Deployment | 部署 / 落地 | Rollout, Go-Live |\n| Pilot Program | 试点项目 | PoC, Proof of Concept |\n| Units Shipped | 出货量 | Shipments |\n| Task Success Rate | 任务成功率 | Completion Rate |\n| Mean Time Between Failures | 平均故障间隔 | MTBF |\n| Total Cost of Ownership | 总拥有成本 | TCO |\n| Bill of Materials | 物料清单 | BOM |\n| Series A/B/C | A/B/C轮融资 | Funding Round |\n| Valuation | 估值 | Pre-money, Post-money |\n| Total Addressable Market | 总可寻址市场 | TAM |\n| Annual Recurring Revenue | 年度经常性收入 | ARR |\n| Robot-as-a-Service | 机器人即服务 | RaaS |\n\n### 8.4 Policy & Safety Terms\n\n| English | Chinese | Aliases / Variants |\n|---------|---------|-------------------|\n| ISO 10218 | — | Industrial Robot Safety |\n| ISO 13482 | — | Personal Care Robot Safety |\n| ISO/TS 15066 | — | Collaborative Robot Safety |\n| EU AI Act | 欧盟人工智能法案 | European AI Regulation |\n| CE Marking | CE认证 | European Conformity |\n| Export Control | 出口管制 | Sanctions, Entity List |\n| Functional Safety | 功能安全 | SIL, Safety Integrity Level |\n| Risk Assessment | 风险评估 | Hazard Analysis |\n\n---\n\n## 9. Relationship Maps\n\n### 9.1 Technology Stack (Bottom-Up)\n\n```\n┌─────────────────────────────────────────────────────────┐\n│                    APPLICATION LAYER                     │\n│  Factory │ Warehouse │ Household │ Healthcare │ Agri    │\n├─────────────────────────────────────────────────────────┤\n│                    INTELLIGENCE LAYER                    │\n│  VLA │ Diffusion Policy │ World Model │ RL │ LLM Plan  │\n├─────────────────────────────────────────────────────────┤\n│                    PERCEPTION LAYER                      │\n│  RGB │ Depth │ Tactile │ Proprioception │ Language      │\n├─────────────────────────────────────────────────────────┤\n│                    CONTROL LAYER                         │\n│  Whole-Body Control │ Impedance │ MPC │ Joint PD        │\n├─────────────────────────────────────────────────────────┤\n│                    HARDWARE LAYER                        │\n│  Actuators │ Sensors │ Compute │ Power │ Structure      │\n├─────────────────────────────────────────────────────────┤\n│                    INFRASTRUCTURE LAYER                  │\n│  Simulation │ Datasets │ Benchmarks │ ROS │ Cloud       │\n└─────────────────────────────────────────────────────────┘\n```\n\n### 9.2 Data Flywheel\n\n```\n┌──────────────┐     ┌──────────────┐     ┌──────────────┐\n│  Collect Data │────▶│  Train Model │────▶│   Deploy     │\n│  (Teleop/Sim) │     │  (VLA/RL/IL) │     │  (Real World)│\n└──────────────┘     └──────────────┘     └──────┬───────┘\n       ▲                                          │\n       │              ┌──────────────┐            │\n       └──────────────│  More Data   │◀───────────┘\n                      │  (Auto-Collect)│\n                      └──────────────┘\n```\n\n### 9.3 Company Landscape Map\n\n```\n┌─────────────────────────────────────────────────────────────────┐\n│                        FULL-STACK                                │\n│  (Hardware + Software + Deployment)                             │\n│                                                                 │\n│  Tesla Optimus │ Figure │ 1X │ Agility │ Apptronik │ Sanctuary │\n│  AGIBOT │ UBTECH │ Unitree │ Fourier │ Galbot │ Kepler        │\n├─────────────────────────────────────────────────────────────────┤\n│                     SOFTWARE / BRAIN                             │\n│  (Foundation Models & Intelligence)                             │\n│                                                                 │\n│  Physical Intelligence │ Skild AI │ DeepMind │ TRI │ Covariant │\n│  HuggingFace (LeRobot) │ Shanghai AI Lab (CLOVER)              │\n├─────────────────────────────────────────────────────────────────┤\n│                     PLATFORM / INFRA                             │\n│  (Simulation, Compute, Tools)                                   │\n│                                                                 │\n│  NVIDIA (Isaac/GR00T) │ Meta (Habitat) │ MuJoCo │ ROS/Open Rob │\n├─────────────────────────────────────────────────────────────────┤\n│                     COMPONENTS / SUPPLY CHAIN                    │\n│  (Actuators, Sensors, Hands)                                    │\n│                                                                 │\n│  Harmonic Drive │ 绿的谐波 │ 双环传动 │ PSYONIC │ Inspire Hand │\n│  GelSight │ Robotiq │ OnRobot                                   │\n└─────────────────────────────────────────────────────────────────┘\n```\n\n---\n\n## 10. Versioning & Maintenance\n\n### Current Version\n- **Version**: 1.0\n- **Created**: February 2026\n- **Last Updated**: February 2026\n\n### Maintenance Schedule\n- **Weekly**: Note new model names, company names, product names as they appear (add to monthly proposal)\n- **Monthly**: Present category tree changes via the Maintenance Proposal workflow (`workflow.md` Part B); require user approval before applying\n- **Quarterly**: Full taxonomy audit; propose company table updates and conference date refreshes\n\n⚠️ Do not modify this file directly without explicit user approval.\n\n### Changelog Template\n```\n## Changelog\n\n### v1.1 — [Date]\n- Added: [new term/company/category]\n- Changed: [reclassified X from Y to Z]\n- Removed: [deprecated term]\n- Notes: [reason for change]\n```\n\n### Known Gaps (To Be Filled)\n- [ ] Detailed supply chain component taxonomy (bearings, encoders, cables)\n- [ ] Comprehensive Chinese academic lab list\n- [ ] Emerging companies tracker (pre-Series A, stealth mode)\n- [ ] Detailed safety standards mapping per region (US/EU/CN/JP/KR)\n- [ ] Humanoid robot spec comparison table (height, weight, DoF, payload, battery, price)\n\n---\n\n> **This file is the \"shared language\" of the entire system.**\n> When in doubt about how to classify a story, consult this file first.\n> When a new term appears that doesn't fit, add it here before using it elsewhere.\n\nFile v1.0.5:references/workflow.md\n\n# 🧭 Embodied AI News — Workflow SOP\n\nA step-by-step Standard Operating Procedure that connects all system files into an executable daily/weekly/monthly workflow.\n\n---\n\n## System File Map\n\n```\n📁 Embodied AI News System\n│\n├── 📰 news_sources.md         — WHERE to find information\n├── 🔍 search_queries.md       — HOW to search for information\n├── ⭐ github_repos.md         — GitHub hot repos module (embodied AI open source)\n├── 📝 output_templates.md     — WHAT format to output\n└── 🧭 workflow.md             — WHEN and in what ORDER to do it (this file)\n```\n\n**How they connect:**\n\n```\n┌─────────────────┐     ┌──────────────────┐     ┌───────────────┐     ┌──────────────────┐\n│  search_queries  │────▶│  news_sources     │────▶│  Classify &   │────▶│ output_templates  │\n│  (discover)      │     │  (browse & verify) │     │  Prioritize   │     │  (generate)       │\n└─────────────────┘     └──────────────────┘     └───────────────┘     └──────────────────┘\n```\n\n---\n\n## Daily Workflow (~30 minutes)\n\n**Goal**: Produce a daily briefing covering the last 24 hours.\n**Output Template**: `Standard Format` or `Brief Format`\n**Best Time**: Morning (8:00–9:00 AM)\n\n---\n\n### Step 1: Search & Discover (10 min)\n\n**Use**: `search_queries.md` → **Recipe A: Daily Briefing**\n\nExecute the 5 queries in order:\n\n| # | Query Focus | Action |\n|---|-------------|--------|\n| Q1 | General embodied AI news | Scan top 10 results, bookmark relevant |\n| Q2 | Foundation model / algorithm news | Scan top 10, bookmark papers & announcements |\n| Q3 | Key company names | Scan top 10, bookmark demos & updates |\n| Q4 | Funding & startup news | Scan top 10, bookmark deals |\n| Q5 | Core media sites | Scan headlines on Robot Report & IEEE Spectrum |\n\n**Output of this step**: A raw list of 10–20 bookmarked URLs.\n\n---\n\n### Step 2: Browse Priority Sources (10 min)\n\n**Use**: `news_sources.md` → **Tier 1 (Core Media)** + **Tier 2 (Company Blogs)**\n\n| Source | Action | Time |\n|--------|--------|------|\n| The Robot Report | Scan homepage headlines | 2 min |\n| IEEE Spectrum Robotics | Scan latest articles | 2 min |\n| TechCrunch Robotics | Check for funding/launch news | 1 min |\n| Tesla AI (X/Twitter) | Check for Optimus updates | 1 min |\n| Figure AI / 1X / Unitree (X) | Check for new demos | 2 min |\n| QbitAI (量子位) | Scan for China ecosystem news | 2 min |\n\n**Output of this step**: Additional 5–10 URLs added to the raw list. Duplicates removed.\n\n---\n\n### Step 2b: GitHub Hot Repos (Optional, +5–10 min)\n\n**When**: User asked for GitHub / 开源仓库雷达, or weekly/monthly brief explicitly includes open-source momentum.\n\n**Use**: `search_queries.md` → **Recipe F** + `github_repos.md` (full G1–G5 procedure).\n\n**Output**: 5–8 verified `https://github.com/owner/repo` rows for **`output_templates.md`** → **⭐ GitHub 热门开源** (place before Key Takeaways).\n\n---\n\n### Step 3: Classify & Prioritize (5 min)\n\nSort all collected stories into the following categories:\n\n| Category | Icon | Priority Criteria |\n|----------|------|-------------------|\n| Major Announcements | 🔥 | New product launch, major demo, paradigm shift |\n| Foundation Models & Algorithms | 🧠 | New model release, SOTA result, open-source drop |\n| Hardware & Platforms | 🦾 | New robot, component breakthrough, spec upgrade |\n| Deployments & Commercial | 🏭 | Real-world deployment, customer announcement |\n| Simulation & Infrastructure | 🌐 | Sim platform update, new benchmark, dataset |\n| Funding & M&A | 💰 | Funding round, acquisition, IPO |\n| Policy, Safety & Ethics | 🌍 | Regulation, safety standard, export control |\n| China Ecosystem | 🇨🇳 | China-specific company, policy, or supply chain |\n\n**Prioritization rules:**\n1. **Lead story**: The single most impactful news item → goes to \"Major Announcements\"\n2. **Must-include**: Any story with >3 sources covering it\n3. **Skip**: Press releases with no substance, duplicate coverage, tangentially related stories\n4. **Target**: 5–8 stories total for a daily briefing\n\n**Output of this step**: A classified, prioritized list of 5–8 stories with categories assigned.\n\n---\n\n### Step 4: Generate Output (5 min)\n\n**Use**: `output_templates.md` → **Standard Format** (default) or **Brief Format** (if time-constrained)\n\nFor each story, fill in the template fields:\n\n```\nFor Standard Format, each story needs:\n├── Headline\n├── Summary (1 sentence)\n├── Key Points (2-3 bullets)\n├── Domain-specific metadata\n│   ├── Robot/Platform (if applicable)\n│   ├── Tech Stack (if applicable)\n│   ├── Model Type / Hardware Type / Deployment Scale (by category)\n│   └── Open Source status (for research stories)\n├── Impact (1-2 sentences)\n├── Source + Date\n└── Link\n```\n\n**Final checks before publishing:**\n- [ ] All links are working\n- [ ] Dates are accurate\n- [ ] No duplicate stories\n- [ ] Key Takeaways section is filled (3 bullets)\n- [ ] Daily Pulse stats table is populated\n\n---\n\n## Weekly Workflow (~90 minutes)\n\n**Goal**: Produce a comprehensive weekly analysis.\n**Output Template**: `Deep Format`\n**Best Time**: Friday afternoon or Saturday morning\n\n---\n\n### Step 1: Aggregate the Week's Daily Briefings (10 min)\n\n- Review all 5 daily briefings from the week\n- Identify the **top 3 stories** that had the most lasting impact\n- Note any **developing stories** that evolved across multiple days\n- Flag stories that deserve deeper analysis\n\n---\n\n### Step 2: Research Deep Dive (30 min)\n\n**Use**: `search_queries.md` → **Recipe B: Weekly Research Deep Dive**\n\n| # | Query Focus | Action |\n|---|-------------|--------|\n| Q1 | arXiv cs.RO embodied AI papers | Identify top 3-5 papers by discussion volume |\n| Q2 | Specific algorithm topics | Find notable diffusion policy / world model papers |\n| Q3 | Sim-to-real & generalist policies | Track cross-embodiment transfer progress |\n| Q4 | Open-source releases | Check for new code/model drops |\n\n**Paper evaluation criteria:**\n- Cited/discussed on X/Twitter by >5 researchers\n- From a top lab (DeepMind, TRI, Stanford, CMU, Berkeley, Tsinghua)\n- Introduces a new benchmark or achieves clear SOTA\n- Includes open-source code or model weights\n\n---\n\n### Step 3: Commercial & Deployment Tracking (20 min)\n\n**Use**: `search_queries.md` → **Recipe C: Commercial & Deployment Tracker**\n\nTrack and update:\n- [ ] New deployment announcements (company → customer, scale, tasks)\n- [ ] Funding rounds closed this week (amount, valuation, investors)\n- [ ] Partnership or M&A activity\n- [ ] Supply chain developments (new components, manufacturing capacity)\n\n---\n\n### Step 4: China Ecosystem Check (15 min)\n\n**Use**: `search_queries.md` → **Recipe D: China Ecosystem Focus**\n\n- [ ] Check Unitree, AGIBOT, UBTECH, Galbot, Fourier for updates\n- [ ] Scan QbitAI and Synced Review for China-specific stories\n- [ ] Check for policy announcements (subsidies, standards, industrial parks)\n- [ ] Note any supply chain or export control developments\n\n---\n\n### Step 5: Generate Weekly Deep Dive (15 min)\n\n**Use**: `output_templates.md` → **Deep Format**\n\nAdditional sections to fill for weekly output:\n- [ ] **Benchmark / Performance tables** for research stories\n- [ ] **Expert Reactions** section (check X/Twitter, blog posts)\n- [ ] **Analysis & Insights** section:\n  - Biggest story of the week\n  - 3 emerging trends\n  - Technology convergence map\n  - What to watch (this week / this month / this quarter)\n- [ ] **Daily Pulse** stats table with weekly aggregates\n\n---\n\n## Monthly Workflow (~3 hours)\n\n**Goal**: Produce a monthly trend report and update the system itself.\n**Output Template**: `Deep Format` + custom monthly summary section\n**Best Time**: First Monday of the new month\n\n---\n\n### Part A: Monthly Trend Report (2 hours)\n\n#### Step 1: Review All Weekly Reports (20 min)\n- Identify the **top 5 stories** of the month\n- Track **recurring themes** across weeks\n- Note **surprises** — things that weren't on the radar at month start\n\n#### Step 2: Thematic Deep Dives (60 min)\n\nChoose 2–3 themes for deeper analysis. Common monthly themes:\n\n| Theme | What to Analyze |\n|-------|----------------|\n| **Model Architecture Trends** | Which approaches are gaining traction? VLA vs. Diffusion vs. World Model? |\n| **Hardware Race** | Who launched new platforms? How do specs compare? Price trends? |\n| **Deployment Scoreboard** | Total units deployed across companies. New verticals entered. |\n| **Funding Landscape** | Total $ raised. Valuation trends. New entrants vs. follow-on rounds. |\n| **China vs. US** | Capability gap analysis. Policy divergence. Supply chain dynamics. |\n| **Open Source Momentum** | New open-source releases. Community adoption metrics. Run **Recipe F** + `github_repos.md` for a **⭐ GitHub** leaderboard section. |\n\n#### Step 3: Generate Monthly Report (40 min)\n\nStructure:\n```\n# 📊 Embodied AI Monthly Report — [Month Year]\n\n## Executive Summary (5 bullets)\n## Top 5 Stories of the Month\n## Thematic Deep Dive 1: [Theme]\n## Thematic Deep Dive 2: [Theme]\n## Thematic Deep Dive 3: [Theme]\n## Funding & Deal Tracker (table)\n## Deployment Tracker (table)\n## Paper Highlights (top 5 papers)\n## What to Watch Next Month\n## Monthly Statistics Dashboard\n```\n\n---\n\n### Part B: System Maintenance Proposal (1 hour)\n\n⚠️ **CRITICAL**: During this phase, the agent does **NOT** directly modify any reference files. Instead, the agent produces a **Maintenance Proposal** — a structured diff of suggested changes — and presents it to you for explicit approval. No reference file may be edited without your explicit consent.\n\n#### Step 1: Audit `news_sources.md` (20 min)\n- [ ] **Identify** new sources discovered during the month\n- [ ] **Flag** sources that have gone inactive or dropped quality\n- [ ] **Note** sources that need re-tiering\n- [ ] **List** new company blogs for emerging players\n- [ ] **Verify** all URLs still work; flag broken links\n\n**Output**: A bullet list of proposed additions, removals, and re-tierings — do NOT write to the file yet.\n\n#### Step 2: Audit `search_queries.md` + `github_repos.md` (20 min)\n- [ ] **List** new company names that emerged this month\n- [ ] **List** new technical terms (e.g., a new model architecture name)\n- [ ] **Flag** queries that consistently return noise\n- [ ] **Flag** queries that return too few or too many results\n- [ ] **Note** conference names needing year updates (e.g., \"CoRL 2026\" → \"CoRL 2027\")\n- [ ] **`github_repos.md`**: note anchor repos, Topic URLs, and Recipe F drift\n\n**Output**: A bullet list of proposed query additions, modifications, and retirements — do NOT write yet.\n\n#### Step 3: Audit `output_templates.md` (10 min)\n- [ ] **Note** new metadata fields that have become important\n- [ ] **Note** category name shifts\n- [ ] **Flag** template gaps for uncovered use cases\n\n**Output**: A bullet list of proposed template changes.\n\n#### Step 4: Audit `workflow.md` (10 min)\n- [ ] **Note** time estimate adjustments based on actual experience\n- [ ] **Note** steps to add/remove\n- [ ] **Note** priority source shifts\n\n**Output**: A bullet list of proposed workflow changes.\n\n#### Step 5: Present Maintenance Proposal to User\n\nCompile Steps 1–4 into a single **Maintenance Proposal** with the following structure:\n\n```markdown\n## 🛠️ Monthly Maintenance Proposal — [Month Year]\n\n### Proposed changes to `news_sources.md`\n- **Add**: [...]\n- **Remove**: [...]\n- **Re-tier**: [...]\n\n### Proposed changes to `search_queries.md`\n- **Add**: [...]\n- **Modify**: [...]\n- **Retire**: [...]\n\n### Proposed changes to `github_repos.md`\n- [...]\n\n### Proposed changes to `output_templates.md`\n- [...]\n\n### Proposed changes to `workflow.md`\n- [...]\n\n### ⚠️ Execute?\nReview the changes above and reply with:\n- \"Apply all\" — to apply everything\n- \"Apply [specific items]\" — to apply selected changes\n- \"Skip\" — to skip maintenance this month\n- \"Show diff for [item]\" — to see the exact diff before approving\n```\n\n**Do NOT write to any reference file until the user explicitly approves the proposal.** Once approved, apply the changes one file at a time and confirm each with a brief summary.\n\n---\n\n## Quarterly Workflow (Half Day)\n\n**Goal**: Strategic review and system overhaul.\n**Best Time**: First week of Q1/Q2/Q3/Q4\n\n---\n\n### Step 1: Conference Season Alignment (30 min)\n\nCheck upcoming conferences and align tracking:\n\n| Quarter | Key Events | Action |\n|---------|-----------|--------|\n| **Q1** (Jan–Mar) | CES, NVIDIA GTC | Track product demos, platform announcements |\n| **Q2** (Apr–Jun) | ICRA, Google I/O, Automate | Track academic papers, Google robotics updates |\n| **Q3** (Jul–Sep) | RSS, RoboCup, WRC (China) | Track research results, China ecosystem |\n| **Q4** (Oct–Dec) | IROS, CoRL, NeurIPS | Track robot learning papers, year-end reviews |\n\n- [ ] Add conference-specific queries to `search_queries.md`\n- [ ] Set calendar reminders for key dates\n- [ ] Identify which companies are likely to announce at each event\n\n---\n\n### Step 2: Competitive Landscape Update (60 min)\n\nBuild/update a company tracker:\n\n```\n| Company | Latest Robot | Gen | Units Deployed | Last Funding | Valuation | Key Tech |\n|---------|-------------|-----|----------------|-------------|-----------|----------|\n| Tesla | Optimus Gen-X | X | ~X,XXX | N/A (public) | — | End-to-end NN |\n| Figure | Figure 0X | X | ~XXX | $X.XB Series X | $XXB | VLA + LLM |\n| ... | ... | ... | ... | ... | ... | ... |\n```\n\n---\n\n### Step 3: Technology Roadmap Review (60 min)\n\nAssess progress on key technical milestones:\n\n| Capability | Status | Key Blockers | Leading Approaches |\n|-----------|--------|-------------|-------------------|\n| Reliable bipedal walking | ✅ Solved | — | Model-based + RL |\n| Dexterous in-hand manipulation | 🟡 Emerging | Tactile sensing, sim gap | RL + Tactile + Sim-to-Real |\n| Language-conditioned task execution | 🟡 Emerging | Grounding, long-horizon | VLA, LLM planning |\n| Generalist multi-task policy | 🔴 Early | Data scale, generalization | Cross-embodiment pretraining |\n| Household autonomy (>1 hour) | 🔴 Early | Long-horizon, recovery | World models, hierarchical |\n\n---\n\n### Step 4: System Overhaul (60 min)\n\n- [ ] Full review of all reference files (including `github_repos.md`)\n- [ ] Archive outdated content\n- [ ] Restructure categories if the field has shifted\n- [ ] Write a \"Quarterly State of Embodied AI\" summary (1 page)\n\n---\n\n## Quick Reference: Which Workflow When?\n\n| Cadence | Time | Template | Recipe | Key Deliverable |\n|---------|------|----------|--------|----------------|\n| **Daily** | 30 min | Standard / Brief | Recipe A | Daily Briefing |\n| **Weekly** | 90 min | Deep | Recipe A+B+C+D | Weekly Analysis |\n| **Monthly** | 3 hours | Deep + Custom | All Recipes | Monthly Trend Report + System Update |\n| **Quarterly** | Half day | Custom | All Recipes | Strategic Review + System Overhaul |\n\n---\n\n## Automation Opportunities\n\n### What Can Be Automated\n| Task | Tool / Method | Difficulty |\n|------|--------------|------------|\n| Query execution | Google Alerts, RSS feeds, custom scripts | Easy |\n| arXiv paper monitoring | Semantic Scholar alerts, arxiv-sanity | Easy |\n| Social media monitoring | X/Twitter lists, Nuzzel-like tools | Easy |\n| Story deduplication | URL matching, title similarity | Medium |\n| Category classification | LLM-based classification prompt | Medium |\n| Template filling | LLM with structured output | Medium |\n| Trend detection | Keyword frequency analysis over time | Hard |\n\n### Recommended Automation Stack\n```\nLayer 1 — Ingestion:\n├── Google Alerts (general queries, daily email digest)\n├── RSS Reader (Feedly / Inoreader) for Tier 1 & Tier 2 sources\n├── Semantic Scholar Alerts (arXiv paper tracking)\n└── X/Twitter Lists (company accounts + key researchers)\n\nLayer 2 — Processing:\n├── LLM (classify, summarize, extract metadata)\n├── Deduplication (URL + title matching)\n└── Priority scoring (source tier × recency × discussion volume)\n\nLayer 3 — Output:\n├── Template rendering (fill output_templates.md)\n├── Distribution (email, Slack, Notion, blog)\n└── Archive (searchable database of past briefings)\n```\n\n---\n\n## Troubleshooting\n\n| Problem | Cause | Solution |\n|---------|-------|---------|\n| Too few results | Queries too narrow | Expand date range; use broader terms; check more sources |\n| Too many results | Queries too broad | Add NOT filters; narrow date range; add domain-specific terms |\n| Missing China news | English-only queries | Add Chinese keywords; check QbitAI/Synced directly |\n| Stale company blogs | Infrequent posting | Rely on X/Twitter + media coverage instead |\n| Duplicate stories | Multiple outlets covering same event | Deduplicate by topic, keep the most detailed source |\n| Can't assess paper importance | No citation data yet (preprint) | Use X/Twitter discussion volume as a proxy |\n| Template too long | Too many stories included | Use Brief format; raise the inclusion threshold to top 5 only |\n\n---\n\n> **Last Updated**: February 2026\n> **Review Cycle**: Monthly (Part B of Monthly Workflow)\n\nFile v1.0.5:skill-card.md\n\n## Description:\n\nAggregates public Embodied AI and robotics sources into structured briefings on humanoid robots, foundation models, hardware, deployments, funding, and optional GitHub open-source activity.\n\nThis skill is ready for commercial/non-commercial use.\n\n## Publisher:\n\n[hexavi8](https://clawhub.ai/user/hexavi8)\n\n### License/Terms of Use:\n\nMIT-0\n\n## Use Case:\n\nResearchers, developers, analysts, and business teams use this skill to request current embodied AI and robotics briefings, topic deep dives, company spotlights, and open-source repository radar with source links.\n\n### Deployment Geography for Use:\n\nGlobal\n\n## Known Risks and Mitigations:\n\nRisk: Briefings can become outdated or reflect incomplete public reporting.\n\nMitigation: Verify publication dates, include direct source links, deduplicate stories, and mark limited or conflicting details instead of filling gaps.\n\nRisk: Information gathering could drift into private, authenticated, or unreliable sources.\n\nMitigation: Use public sources from the curated reference files, skip content requiring authentication, and do not use private accounts or tokens.\n\nRisk: Proposed edits to the skill's own reference files could change future briefing behavior.\n\nMitigation: Keep briefing output separate from maintenance work and require explicit user approval and review before applying reference-file changes.\n\n## Reference(s):\n\n- [ClawHub Skill Page](https://clawhub.ai/hexavi8/skills/embodied-ai-news)\n- [Publisher Profile](https://clawhub.ai/user/hexavi8)\n- [Skill Homepage](https://github.com/HeXavi8/skills)\n- [News Source Guide](references/news_sources.md)\n- [Search Query Recipes](references/search_queries.md)\n- [Output Format Templates](references/output_templates.md)\n- [Domain Taxonomy](references/taxonomy.md)\n- [Workflow SOP](references/workflow.md)\n- [GitHub Repository Radar Procedure](references/github_repos.md)\n\n## Skill Output:\n\n**Output Type(s):** [text, markdown, guidance]\n\n**Output Format:** [Markdown briefing with categorized sections, source links, and optional GitHub repository table]\n\n**Output Parameters:** [1D]\n\n**Other Properties Related to Output:** [Supports daily, weekly, monthly, custom topic, company-specific, China ecosystem, and GitHub open-source radar briefings.]\n\n## Skill Version(s):\n\n1.0.5 (source: ClawHub release evidence)\n\n## Ethical Considerations:\n\nUsers should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.\n\nArchive v1.0.4: 8 files, 51765 bytes\n\nFiles: references/github_repos.md (5787b), references/news_sources.md (21821b), references/output_templates.md (25475b), references/search_queries.md (18904b), references/taxonomy.md (30726b), references/workflow.md (16048b), SKILL.md (23375b), _meta.json (135b)\n\nFile v1.0.4:SKILL.md\n\n---\nname: embodied-ai-news\ndescription: \"Aggregates publicly available Embodied AI and Robotics news from curated sources (robotics media, arXiv, company blogs). Delivers structured briefings on humanoid robots, foundation models, hardware, deployments, and funding with direct links to original articles. Optional module surfaces hot GitHub open-source repos relevant to embodied AI (policies, sim, data, benchmarks).\"\nhomepag\n\nArchive v1.0.3: 7 files, 45392 bytes\n\nFiles: references/news_sources.md (21821b), references/output_templates.md (23570b), references/search_queries.md (16575b), references/taxonomy.md (30726b), references/workflow.md (15285b), SKILL.md (20125b), _meta.json (135b)\n\nArchive v1.0.2: 7 files, 51354 bytes\n\nFiles: references/news_sources.md (21821b), references/output_templates.md (23570b), references/search_queries.md (16575b), references/taxonomy.md (30726b), references/workflow.md (15285b), SKILL.md (37947b), _meta.json (135b)\n\nArchive v1.0.1: 7 files, 52096 bytes\n\nFiles: references/news_source.md (21821b), references/output_templates.md (23570b), references/search_queries.md (16575b), references/taxonomy.md (30726b), references/workflow.md (15285b), SKILL.md (40586b), _meta.json (135b)\n\nArchive v1.0.0: 7 files, 50711 bytes\n\nFiles: references/news_source.md (21821b), references/output_templates.md (23570b), references/search_queries.md (16575b), references/taxonomy.md (30726b), references/workflow.md (15285b), SKILL.md (36442b), _meta.json (135b)","readmeExcerpt":"Skill: Embodied Ai News Owner: hexavi8 Summary: Aggregates publicly available Embodied AI and Robotics news from curated sources (robotics media, arXiv, company blogs). Delivers structured briefings on hum... Tags: VLA:1.0.3, ai:1.0.3, benchmarks:1.0.3, deployment:1.0.3, dexterous-manipulation:1.0.3, diffusion-policy:1.0.3, embodied-ai:1.0.3, foundation-models:1.0.3, funding:1.0.3, hardware:1.0.3, humanoid-robot:1.0.","codeSnippets":[],"executableExamples":[{"language":"text","snippet":"📁 references/\n├── 📰 news_sources.md        — WHERE to find information (tiered source list)\n├── 🔍 search_queries.md     — HOW to search (query templates & recipes)\n├── 📝 output_templates.md   — WHAT format to output (6+ template variants)\n├── 📊 taxonomy.md           — SHARED LANGUAGE (categories, keywords, company list)\n├── ⭐ github_repos.md       — GitHub hot repos module (discovery, ranking, output schema)\n└── 🧭 workflow.md           — WHEN and in what ORDER to execute (SOP for daily/weekly/monthly)"},{"language":"text","snippet":"┌─────────────────┐      ┌────────────────────┐     ┌───────────────┐     ┌──────────────────┐\n│  search_queries │────▶ │  news_sources      │────▶│  Classify &   │────▶│ output_templates │\n│  (discover)     │      │  (browse & verify) │     │  Prioritize   │     │   (generate)     │\n└─────────────────┘      └────────────────────┘     └───────────────┘     └──────────────────┘\n                                    ▲                        ▲\n                                    │                        │\n                                    └────── taxonomy.md ─────┘\n                                         (shared vocabulary)\n\nOptional GitHub module:\n  search_queries (Recipe F) ──▶ github_repos.md ──▶ output_templates (⭐ GitHub section)"},{"language":"text","snippet":"cat:cs.RO AND (\"embodied AI\" OR \"robot learning\" OR \"VLA\") submittedDate:[today - 7d TO today]"},{"language":"markdown","snippet":"# 🤖 Embodied AI Daily Briefing\n\n**Date**: [Current Date, e.g., February 23, 2026]\n**Sources**: [X] articles from [Y] sources\n**Coverage**: Last 24 hours\n\n---\n\n## 🔥 Major Announcements\n\n### [Headline 1]\n\n**Summary**: [One-sentence overview of the news]\n\n**Key Points**:\n- [Important detail 1]\n- [Important detail 2]\n- [Important detail 3]\n\n**Robot/Platform**: [e.g., Optimus Gen-3 / Digit v3 / GR00T — or \"N/A\" if not platform-specific]\n**Tech Stack**: [e.g., VLA Model + Dexterous Hand / RL Policy + Sim-to-Real / End-to-End Transformer]\n**Impact**: [Why this matters for the embodied AI field — 1-2 sentences]\n\n📅 **Source**: [Publication Name] • [Publication Date]\n🔗 **Link**: [URL]\n\n---\n\n### [Headline 2]\n\n**Summary**: [One-sentence overview]\n\n**Key Points**:\n- [Important detail 1]\n- [Important detail 2]\n\n**Robot/Platform**: [Platform name or \"N/A\"]\n**Tech Stack**: [Key technologies involved]\n**Impact**: [Why this matters]\n\n📅 **Source**: [Publication Name] • [Publication Date]\n🔗 **Link**: [URL]\n\n---\n\n## 🧠 Foundation Models & Algorithms\n\n### [Headline 3]\n\n**Summary**: [One-sentence overview]\n\n**Key Points**:\n- [Important detail 1]\n- [Important detail 2]\n- [Important detail 3]\n\n**Model Type**: [VLA / World Model / Diffusion Policy / RL / Imitation Learning / Other]\n**Embodiment**: [Humanoid / Manipulator / Quadruped / Multi-embodiment / Simulation-only]\n**Open Source**: [Yes — GitHub link / No / Partial]\n**Impact**: [Why this matters]\n\n📅 **Source**: [Publication Name] • [Publication Date]\n🔗 **Link**: [URL]\n\n---\n\n## 🦾 Hardware & Platforms\n\n### [Headline 4]\n\n**Summary**: [One-sentence overview]\n\n**Key Points**:\n- [Important detail 1]\n- [Important detail 2]\n\n**Hardware Type**: [Humanoid / Dexterous Hand / Actuator / Sensor / Compute Module / Full Platform]\n**Company**: [Company name]\n**Specs**: [Key specs if available — DoF, payload, battery life, etc.]\n**Impact**: [Why this matters]\n\n📅 **Source**: [Publication Name] • [Publication Date]\n🔗 **Link**: [URL]\n\n---\n\n## 🏭"},{"language":"markdown","snippet":"# 🤖 Embodied AI Headlines\n\n**Date**: [Current Date]\n**Coverage**: Last 24 hours\n\n## 🔥 Major Announcements\n\n• [Headline 1] — *[Company/Lab]* ([Publication])\n🔗 [URL]\n\n• [Headline 2] — *[Company/Lab]* ([Publication])\n🔗 [URL]\n\n---\n\n## 🧠 Foundation Models & Algorithms\n\n• [Headline 3] — *[Model Name]* ([Publication])\n🔗 [URL]\n\n• [Headline 4] — *[Model Name]* ([Publication])\n🔗 [URL]\n\n---\n\n## 🦾 Hardware & Platforms\n\n• [Headline 5] — *[Robot/Component]* ([Publication])\n🔗 [URL]\n\n---\n\n## 🏭 Deployments & Commercial\n\n• [Headline 6] — *[Company → Customer]* ([Publication])\n🔗 [URL]\n\n---\n\n## 💰 Funding & M&A\n\n• [Headline 7] — *[Company] raises $[X]M* ([Publication])\n🔗 [URL]\n\n---\n\n## 🇨🇳 China Ecosystem\n\n• [Headline 8] — *[Company/Lab]* ([Publication])\n🔗 [URL]\n\n---\n\n## ⭐ GitHub 热门开源（可选）\n\n> Omit this entire section if the GitHub module was not run.\n\n• [`owner/repo`](https://github.com/owner/repo) — *[Category tag]* — [≤15 words]\n• [`owner/repo`](https://github.com/owner/repo) — *[Category tag]* — [≤15 words]\n• *(3–6 more as needed; 5–8 total)*\n\n---\n\n**Quick Summary**: [2-3 sentence overview of the day's most important Embodied AI news]\n\n**Generated on**: [Timestamp]"},{"language":"markdown","snippet":"# 📊 Embodied AI Deep Dive\n\n**Date**: [Current Date]\n**Coverage**: Last 24 hours\n**Analysis Depth**: In-depth\n\n---\n\n## 🔥 Major Announcements\n\n### [Headline 1]\n\n**Summary**: [One-sentence overview]\n\n**Key Details**:\n- [Important detail 1]\n- [Important detail 2]\n- [Important detail 3]\n- [Additional detail 4]\n- [Additional detail 5]\n\n**Technical Deep Dive**:\n[2-3 sentences breaking down the technical approach — architecture, training paradigm, key innovations]\n\n**Benchmark / Performance**:\n| Metric | This Work | Previous SOTA | Improvement |\n|--------|-----------|---------------|-------------|\n| [Metric 1] | [Value] | [Value] | [+X%] |\n| [Metric 2] | [Value] | [Value] | [+X%] |\n\n**Impact Analysis**:\n[2-3 sentences analyzing why this matters and its implications for the field]\n\n**Expert Reactions**:\n[Summary of expert opinions from X/Twitter, blog posts, or interviews]\n\n**Context & Background**:\n[How this fits into the broader trajectory — previous work, competing approaches, timeline]\n\n📅 **Source**: [Publication Name] • [Publication Date]\n🔗 **Link**: [URL]\n\n---\n\n## 🧠 Foundation Models & Algorithms\n\n### [Paper/Model Title]\n\n**Summary**: [What this model/algorithm does]\n\n**Architecture**:\n- **Input**: [Vision / Language / Proprioception / Tactile / Multi-modal]\n- **Backbone**: [Transformer / Diffusion / Flow Matching / RL / Hybrid]\n- **Output**: [Actions / Trajectories / Plans / Rewards]\n- **Training**: [Imitation Learning / RL / Self-supervised / Hybrid — data scale if known]\n\n**Key Contributions**:\n- [Contribution 1]\n- [Contribution 2]\n- [Contribution 3]\n\n**Embodiment & Tasks**:\n- **Tested on**: [Robot platform(s)]\n- **Tasks**: [Pick-and-place / Navigation / Dexterous manipulation / Locomotion / Multi-task]\n- **Sim-to-Real**: [Yes/No — transfer method if applicable]\n\n**Results**:\n[Key benchmarks, success rates, generalization capabilities]\n\n**Limitations & Open Questions**:\n[What doesn't work yet, failure modes, scalability concerns]\n\n**Significance**:\n[Why this ad"}],"parameters":null,"dependencies":[],"permissions":[],"extractedFiles":[{"path":"SKILL.md","content":"---\nname: embodied-ai-news\ndescription: \"Aggregates publicly available Embodied AI and Robotics news from curated sources (robotics media, arXiv, company blogs). Delivers structured briefings on humanoid robots, foundation models, hardware, deployments, and funding with direct links to original articles. Optional module surfaces hot GitHub open-source repos relevant to embodied AI (policies, sim, data, benchmarks).\"\nhomepage: https://github.com/HeXavi8/skills\n---\n\n# Embodied AI News Briefing\n\n> Aggregates the latest Embodied AI & Robotics news from curated sources and delivers concise summaries with direct links. Covers the full stack: algorithms, hardware, simulation, deployment, funding, policy, and the China ecosystem.\n\n## When to Use This Skill\n\nActivate this skill when the user:\n\n- Asks for embodied AI news, robot news, or humanoid robot updates\n- Requests a daily/weekly/monthly robotics briefing\n- Mentions wanting to know what's happening in embodied AI / robotics\n- Asks about specific companies: Tesla Optimus, Figure, Unitree, AGIBOT, Boston Dynamics, etc.\n- Asks about specific technologies: VLA models, diffusion policy, sim-to-real, dexterous manipulation\n- Wants a summary of recent robotics research papers\n- Asks about robotics funding, deployments, or supply chain\n- Asks about simulation platforms, benchmarks, or datasets\n- Asks for **GitHub 热门仓库**、**具身智能开源项目**、**star 最多的机器人代码库**，或 wants a **repo leaderboard / open-source radar**\n- Asks about robotics policy, safety standards, or export controls\n- Requests a monthly trend report or competitive analysis\n- Says: \"给我今天的具身智能资讯\" (Give me today's embodied AI news)\n- Says: \"机器人行业有什么新动态\" (What's new in the robot industry)\n- Says: \"最近有什么人形机器人的消息\" (Any recent humanoid robot news)\n- Says: \"这个月的具身智能趋势报告\" (This month's embodied AI trend report)\n- Says: \"embodied AI updates\", \"robot learning news\", \"humanoid robot news\"\n\n### Trigger Keywords\n\n**English**: `embodied AI`, `humanoid robot`, `robot news`, `robotics update`, `robot learning`, `VLA model`, `diffusion policy`, `dexterous manipulation`, `sim-to-real`, `robot deployment`, `robotics funding`, `Figure AI`, `Tesla Optimus`, `Unitree`, `AGIBOT`, `Boston Dynamics`, `1X`, `Physical Intelligence`, `Skild AI`, `robot hand`, `quadruped robot`, `Isaac Sim`, `world model robot`, `robot benchmark`, `robot safety`, `robot regulation`, `monthly robot report`\n\n**Chinese**: `具身智能`, `人形机器人`, `机器人资讯`, `灵巧操作`, `仿真到真实`, `机器人部署`, `宇树`, `智元`, `优必选`, `银河通用`, `傅利叶`, `机器人融资`, `灵巧手`, `四足机器人`, `机器人大模型`, `机器人月报`, `机器人安全`, `机器人政策`, `GitHub 热门`, `开源仓库`, `机器人开源`\n\n---\n\n## Reference Files\n\nThis skill relies on **6** companion reference files. Always consult them during execution:\n\n```\n📁 references/\n├── 📰 news_sources.md        — WHERE to find information (tiered source list)\n├── 🔍 search_queries.md     — HOW to search (query templates & recipes)\n├── 📝 output_templates.md   — WHAT format to output (6+ template variants)\n├── 📊 taxonomy.md           — SHARED LANGUAGE (cate"},{"path":"_meta.json","content":"{\n  \"ownerId\": \"kn7arpc65p9wdnhbw70435rrf181jvtk\",\n  \"slug\": \"embodied-ai-news\",\n  \"version\": \"1.0.5\",\n  \"publishedAt\": 1777795953721\n}"},{"path":"references/github_repos.md","content":"# ⭐ GitHub — Embodied AI Open Source Hot Repos\n\nCompanion reference for the **GitHub 热门开源仓库** module: how to discover, rank, and present repositories that are most relevant to embodied AI / robot learning (not generic industrial automation or unrelated “robot” tooling).\n\n---\n\n## When to Use This File\n\nConsult during **Phase 1** (gathering) and **Phase 5** (output) when:\n\n- The user asks for **GitHub 热门**、**开源仓库**、**star 最多的机器人项目**，或 explicitly wants a **repo leaderboard**\n- The briefing type is **Weekly** or **Monthly** and the user wants **open-source momentum** included\n- You are filling the **`## ⭐ GitHub 热门开源（具身智能相关）`** section in `output_templates.md`\n\n**Default**: Do **not** add this section to a **Daily** briefing unless the user asked for it or for “full stack / 含开源”.\n\n---\n\n## Data Sources & Tools\n\n| Source | Tool | Notes |\n|--------|------|--------|\n| GitHub repository search (sorted) | `WebSearch` + `WebFetch` | Prefer official `github.com` URLs; verify repo still exists |\n| GitHub Topics | `WebFetch` | e.g. topic pages for `robotics`, `reinforcement-learning`, `sim2real` |\n| Curated lists / “awesome-*” | `WebSearch` | Use to cross-check names; still verify primary repo URL on GitHub |\n\n**Do not** fabricate star counts or “#1 trending” claims. Use numbers **only** if visible on the fetched GitHub page or in search snippets at collection time; otherwise write **“stars: see repo page”** or omit the column.\n\n---\n\n## Relevance Filter (Must Pass)\n\nA repo **qualifies** for this module if it clearly supports **one or more** of:\n\n- **Policies / models**: VLA, diffusion / flow policies, imitation / offline RL, world models for control\n- **Data & teleop**: datasets, teleoperation stacks, human demo pipelines\n- **Simulation → real**: Isaac / MuJoCo / Habitat-class stacks, domain randomization, sim benchmarks\n- **Whole-body / manipulation**: humanoid / quadruped / arm stacks where **learning** or **ML policy** is central\n- **Embodied foundation models**: GR00T-class, generalist robot models, cross-embodiment training code\n\n**Deprioritize or exclude** unless the user asks broadly:\n\n- Pure motion planning / classic control with no learning angle\n- Arduino / ROS tutorial repos with no embodied-AI focus\n- Unmanned vehicles / autopilot-only (unless explicitly in scope)\n- Empty forks, archived with no replacement, or name-squatting\n\n---\n\n## Discovery Procedure (Executable)\n\n### Step G1 — Run discovery queries\n\nUse `search_queries.md` → **Section 10.5** and **Recipe F** (`WebSearch`, `return_format`: markdown).\n\nMinimum: **3 queries** from Recipe F (rotate which sub-queries you use if a run returns noise).\n\n### Step G2 — Collect candidates\n\nTarget **12–20** candidate repos, then **shortlist 5–8** for the briefing.\n\n### Step G3 — Rank (“热门” definition)\n\nApply in order (break ties by recency of meaningful commits / releases if visible):\n\n1. **Ecosystem impact**: widely cited stacks (sim, benchmark, policy zoo), de-facto standard tooling\n2. **Recent activity"},{"path":"references/news_sources.md","content":"# 🤖 Embodied AI — Comprehensive News Source Guide\n\nA curated directory of the most authoritative information sources in the Embodied AI space, covering hardware R&D, foundation models, algorithms, commercial deployment, policy, investment, and academic research.\n\n---\n\n## Tier 1: Core Industry Media (Daily Must-Reads)\n\nThese outlets provide dedicated, deep-dive coverage of the intersection of robotics and AI.\n\n### 1. The Robot Report\n- **URL**: [https://www.therobotreport.com/](https://www.therobotreport.com/)\n- **Frequency**: Daily\n- **Focus**: Industrial robots, humanoid robots, commercial deployments, industry earnings\n- **Best For**: Tracking business moves and technology adoption across the robotics industry\n- **Key Strength**: The \"Wall Street Journal\" of robotics — deep commercial analysis and trend reporting.\n\n### 2. IEEE Spectrum — Robotics\n- **URL**: [https://spectrum.ieee.org/topic/robotics/](https://spectrum.ieee.org/topic/robotics/)\n- **Frequency**: Daily\n- **Focus**: Cutting-edge robotics tech, lab prototypes, sensors & actuators\n- **Best For**: Understanding the latest engineering breakthroughs and hardcore technical details\n- **Key Strength**: Operated by IEEE; extremely high technical authority with exclusive interviews (notably Evan Ackerman's coverage).\n\n### 3. TechCrunch — Robotics\n- **URL**: [https://techcrunch.com/category/robotics/](https://techcrunch.com/category/robotics/)\n- **Frequency**: Daily\n- **Focus**: Startup funding rounds, product launches, VC landscape in robotics\n- **Best For**: Tracking investment flows and emerging startups in embodied AI\n- **Key Strength**: First-mover on breaking funding news; strong Silicon Valley network.\n\n### 4. Robotics Business Review (RBR)\n- **URL**: [https://www.roboticsbusinessreview.com/](https://www.roboticsbusinessreview.com/)\n- **Frequency**: Daily\n- **Focus**: Enterprise robotics, ROI analysis, market sizing, automation strategy\n- **Best For**: Business leaders evaluating robotics adoption\n- **Key Strength**: Focused on the business case for robotics, not just the tech.\n\n### 5. RoboticsTomorrow\n- **URL**: [https://www.roboticstomorrow.com/](https://www.roboticstomorrow.com/)\n- **Frequency**: Daily\n- **Focus**: Industry predictions, automation trends, embodied AI outlook\n- **Best For**: Broad industry pulse and expert opinion pieces\n- **Key Strength**: Aggregates expert perspectives from across the robotics ecosystem.\n\n### 6. Interesting Engineering — Robotics\n- **URL**: [https://interestingengineering.com/innovation/robotics](https://interestingengineering.com/innovation/robotics)\n- **Frequency**: Daily\n- **Focus**: Humanoid robots, viral robot demos, hardware innovation\n- **Best For**: Accessible, visual-first reporting on the latest robot developments\n- **Key Strength**: Excellent at capturing viral moments and translating them into context-rich stories.\n\n---\n\n## Tier 2: Top Company Official Blogs (Weekly Focus)\n\nBreakthroughs in embodied AI are often driven by leading compan"},{"path":"references/output_templates.md","content":"# 🤖 Embodied AI News — Output Format Templates\n\nPre-defined templates tailored for the Embodied AI / Robotics domain, covering hardware breakthroughs, foundation models, sim-to-real transfer, commercial deployments, and supply chain dynamics.\n\n---\n\n## Standard Format (Default)\n\nThe most commonly used format with Embodied AI–specific categories.\n\n```markdown\n# 🤖 Embodied AI Daily Briefing\n\n**Date**: [Current Date, e.g., February 23, 2026]\n**Sources**: [X] articles from [Y] sources\n**Coverage**: Last 24 hours\n\n---\n\n## 🔥 Major Announcements\n\n### [Headline 1]\n\n**Summary**: [One-sentence overview of the news]\n\n**Key Points**:\n- [Important detail 1]\n- [Important detail 2]\n- [Important detail 3]\n\n**Robot/Platform**: [e.g., Optimus Gen-3 / Digit v3 / GR00T — or \"N/A\" if not platform-specific]\n**Tech Stack**: [e.g., VLA Model + Dexterous Hand / RL Policy + Sim-to-Real / End-to-End Transformer]\n**Impact**: [Why this matters for the embodied AI field — 1-2 sentences]\n\n📅 **Source**: [Publication Name] • [Publication Date]\n🔗 **Link**: [URL]\n\n---\n\n### [Headline 2]\n\n**Summary**: [One-sentence overview]\n\n**Key Points**:\n- [Important detail 1]\n- [Important detail 2]\n\n**Robot/Platform**: [Platform name or \"N/A\"]\n**Tech Stack**: [Key technologies involved]\n**Impact**: [Why this matters]\n\n📅 **Source**: [Publication Name] • [Publication Date]\n🔗 **Link**: [URL]\n\n---\n\n## 🧠 Foundation Models & Algorithms\n\n### [Headline 3]\n\n**Summary**: [One-sentence overview]\n\n**Key Points**:\n- [Important detail 1]\n- [Important detail 2]\n- [Important detail 3]\n\n**Model Type**: [VLA / World Model / Diffusion Policy / RL / Imitation Learning / Other]\n**Embodiment**: [Humanoid / Manipulator / Quadruped / Multi-embodiment / Simulation-only]\n**Open Source**: [Yes — GitHub link / No / Partial]\n**Impact**: [Why this matters]\n\n📅 **Source**: [Publication Name] • [Publication Date]\n🔗 **Link**: [URL]\n\n---\n\n## 🦾 Hardware & Platforms\n\n### [Headline 4]\n\n**Summary**: [One-sentence overview]\n\n**Key Points**:\n- [Important detail 1]\n- [Important detail 2]\n\n**Hardware Type**: [Humanoid / Dexterous Hand / Actuator / Sensor / Compute Module / Full Platform]\n**Company**: [Company name]\n**Specs**: [Key specs if available — DoF, payload, battery life, etc.]\n**Impact**: [Why this matters]\n\n📅 **Source**: [Publication Name] • [Publication Date]\n🔗 **Link**: [URL]\n\n---\n\n## 🏭 Deployments & Commercial\n\n### [Headline 5]\n\n**Summary**: [One-sentence overview]\n\n**Key Points**:\n- [Important detail 1]\n- [Important detail 2]\n\n**Deployment Scale**: [Pilot / Small batch / Mass production — units if known]\n**Industry Vertical**: [Automotive / Logistics / Manufacturing / Household / Agriculture / Other]\n**Company → Customer**: [e.g., Figure AI → BMW, Agility → Amazon]\n**Impact**: [Why this matters]\n\n📅 **Source**: [Publication Name] • [Publication Date]\n🔗 **Link**: [URL]\n\n---\n\n## 🌐 Simulation & Infrastructure\n\n### [Headline 6]\n\n**Summary**: [One-sentence overview]\n\n**Key Points**:\n- [Important detail 1]\n- [Impor"}],"languages":[],"docsSourceLabel":"CLAWHUB","editorialOverview":"Aggregates publicly available Embodied AI and Robotics news from curated sources (robotics media, arXiv, company blogs). Delivers structured briefings on hum... Skill: Embodied Ai News Owner: hexavi8 Summary: Aggregates publicly available Embodied AI and Robotics news from curated sources (robotics media, arXiv, company blogs). Delivers structured briefings on hum... 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