Embodied AI News

Embodied AI News Briefing

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.

When to Use This Skill

Activate this skill when the user:

• Asks for embodied AI news, robot news, or humanoid robot updates
• Requests a daily/weekly/monthly robotics briefing
• Mentions wanting to know what's happening in embodied AI / robotics
• Asks about specific companies: Tesla Optimus, Figure, Unitree, AGIBOT, Boston Dynamics, etc.
• Asks about specific technologies: VLA models, diffusion policy, sim-to-real, dexterous manipulation
• Wants a summary of recent robotics research papers
• Asks about robotics funding, deployments, or supply chain
• Asks about simulation platforms, benchmarks, or datasets
• Asks about robotics policy, safety standards, or export controls
• Requests a monthly trend report or competitive analysis
• Says: "给我今天的具身智能资讯" (Give me today's embodied AI news)
• Says: "机器人行业有什么新动态" (What's new in the robot industry)
• Says: "最近有什么人形机器人的消息" (Any recent humanoid robot news)
• Says: "这个月的具身智能趋势报告" (This month's embodied AI trend report)
• Says: "embodied AI updates", "robot learning news", "humanoid robot news"

Trigger Keywords

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

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

Reference Files

This skill relies on 5 companion reference files. Always consult them during execution:

📁 references/
├── 📰 news_sources.md        — WHERE to find information (tiered source list)
├── 🔍 search_queries.md     — HOW to search (query templates & recipes)
├── 📝 output_templates.md   — WHAT format to output (6+ template variants)
├── 📊 taxonomy.md           — SHARED LANGUAGE (categories, keywords, company list)
└── 🧭 workflow.md           — WHEN and in what ORDER to execute (SOP for daily/weekly/monthly)

File Interconnection Map

┌─────────────────┐      ┌────────────────────┐     ┌───────────────┐     ┌──────────────────┐
│  search_queries │────▶ │  news_sources      │────▶│  Classify &   │────▶│ output_templates │
│  (discover)     │      │  (browse & verify) │     │  Prioritize   │     │   (generate)     │
└─────────────────┘      └────────────────────┘     └───────────────┘     └──────────────────┘
                                    ▲                        ▲
                                    │                        │
                                    └────── taxonomy.md ─────┘
                                         (shared vocabulary)

Execution Workflow

Phase 0: Determine Briefing Type & Time Scope

Before any tool calls, ask the user (if not already clear):

1. Briefing Type: Daily / Weekly / Monthly / Custom Topic?
2. Time Scope: Last 24 hours / Last 7 days / Last 30 days / Custom date range?
3. Output Format: Standard / Brief / Thread / Markdown Report / Presentation / Custom?
4. Focus Area (optional): All categories / Specific category (e.g., only hardware, only China ecosystem)?

Default if user doesn't specify:

• Type: Daily
• Scope: Last 24 hours
• Format: Standard
• Focus: All categories

Map to workflow.md:

• Daily → workflow.md Section "Daily Workflow"
• Weekly → workflow.md Section "Weekly Workflow"
• Monthly → workflow.md Section "Monthly Workflow"

Phase 1: Information Gathering

Consult workflow.md for the appropriate recipe, then execute the corresponding steps from search_queries.md and news_sources.md.

Step 1.1: Execute Search Queries

Tool: WebSearch (or equivalent web search tool)

Source: search_queries.md → Select the appropriate recipe:

• Daily Briefing → Recipe A (5 queries)
• Weekly Roundup → Recipe B (8 queries)
• Monthly Deep Dive → Recipe C (12 queries)
• Custom Topic → Recipe D + user-specified filters

Parameters:

• return_format: markdown
• with_images_summary: false
• timeout: 20 seconds per source
• Fetch only from publicly accessible sources listed in news_sources.md

Output: A list of 20–50 URLs with headlines and snippets.

Step 1.2: Fetch Tier 1 Sources Directly

Tool: mcp__web_reader__webReader

Source: news_sources.md → Tier 1 section

Directly fetch the homepage or RSS feed of:

• The Robot Report
• IEEE Spectrum — Robotics
• TechCrunch — Robotics
• Robotics Business Review
• (Add others based on briefing type)

Parameters:

• url: [homepage URL from news_sources.md]
• return_format: markdown
• with_images_summary: false
• Process only URLs from verified sources in news_sources.md

Output: Recent headlines (last 24h / 7d / 30d based on scope).

Step 1.3: Fetch arXiv Papers

Tool: mcp__arxiv__readURL (if available) or WebSearch with arXiv-specific queries

Source: search_queries.md → Section "6. Academic Research (arXiv)"

Execute 2–3 arXiv queries:

cat:cs.RO AND ("embodied AI" OR "robot learning" OR "VLA") submittedDate:[today - 7d TO today]

Output: 5–10 recent papers with abstracts.

Step 1.4: Fetch Company Blogs & Official Announcements

Tool: mcp__web_reader__webReader

Source: news_sources.md → Tier 2 (Company Blogs) + Tier 4 (China Ecosystem)

Fetch from:

• Figure AI Blog
• Physical Intelligence Blog
• Tesla AI Blog
• Unitree Blog (Chinese + English)
• AGIBOT WeChat Official Account (if accessible)
• (Add others based on focus area)

Fetch constraints:

• Only process URLs from search results and sources listed in news_sources.md
• Skip content requiring authentication
• Timeout: 15 seconds per URL

Output: Recent announcements (last 7d / 30d based on scope).

Phase 2: Content Extraction & Deduplication

For each fetched URL:

1. Extract:

   • Headline
   • Publication date
   • Source name
   • Summary (first 2–3 paragraphs or abstract)
   • Key entities: companies, models, hardware platforms (use taxonomy.md for reference)

2. Deduplicate:

   • If multiple sources cover the same story, keep the one with the most detail
   • Merge information if they provide complementary details

3. Discard:

   • Stories older than the time scope
   • Irrelevant content (use search_queries.md Section 1.4 "Noise Exclusion Filter")
   • Duplicate announcements

Output: A deduplicated list of 15–30 stories with extracted metadata.

Phase 3: Classification & Prioritization

Consult taxonomy.md to classify each story.

Step 3.1: Assign Primary Category

Use taxonomy.md → Section "1. News Category Taxonomy"

Assign each story to exactly one primary category:

• 🔥 Major Announcements
• 🧠 Foundation Models & Algorithms
• 🦾 Hardware & Platforms
• 🌐 Simulation & Infrastructure
• 🏭 Deployments & Commercial
• 💰 Funding, M&A & Business
• 🌍 Policy, Safety & Ethics
• 🇨🇳 China Ecosystem

Rules (from taxonomy.md → "Category Assignment Rules"):

• Major Announcements: Only for top-impact stories (new paradigm, >$500M funding, first-ever deployment milestone)
• China Ecosystem: Use when the story's primary significance is about the Chinese market/ecosystem
• Cross-cutting stories: Assign primary + up to 2 secondary tags

Step 3.2: Assign Priority Level

Use taxonomy.md → Section "3. Priority Scoring System"

Calculate priority score (0–100) based on:

• Impact (0–40 points): Paradigm shift / Major milestone / Incremental improvement
• Timeliness (0–20 points): Breaking news / Recent (1–3 days) / Older
• Source Authority (0–20 points): Tier 1 / Tier 2 / Tier 3
• Relevance (0–20 points): Core embodied AI / Adjacent / Tangential

Priority Levels:

• P0 (90–100): Must-read, above-the-fold
• P1 (70–89): Important, include in main body
• P2 (50–69): Notable, include if space allows
• P3 (<50): Optional, move to "Other News" section or omit

Step 3.3: Sort Stories

Within each category, sort by:

1. Priority score (descending)
2. Publication date (most recent first)

Phase 4: Content Synthesis

For each story, generate:

1. One-sentence summary: Capture the core news in <20 words

2. Key points (2–4 bullet points): Extract the most important details

3. Metadata fields (based on category):

   • For Foundation Models: Model Type, Embodiment, Open Source, Impact
   • For Hardware: Hardware Type, Company, Specs, Impact
   • For Deployments: Deployment Scale, Industry Vertical, Performance Metrics, Impact
   • For Funding: Amount, Lead Investor, Valuation, Use of Funds
   • (See output_templates.md for full metadata schema per category)

4. Impact statement: Why this matters for the embodied AI field (1–2 sentences)

Tone & Style:

• Objective: Present facts without hype or editorial opinion
• Concise: Favor clarity over completeness
• Technical: Use domain-specific terminology from taxonomy.md
• Neutral: Treat all companies, countries, and technologies equally

Phase 5: Output Generation

Consult output_templates.md to select the appropriate template.

Step 5.1: Select Template

Based on user request (from Phase 0):

Step 5.2: Render Output

Fill in the selected template with:

• Header: Date, source count, time scope
• Category sections: Ordered by priority (🔥 Major Announcements first)
• Story blocks: Headline, summary, key points, metadata, source link
• Footer: Methodology note, source attribution

Quality checks:

• All links are valid and correctly formatted
• All metadata fields are filled (use "N/A" if not applicable)
• No duplicate stories
• Stories are sorted by priority within each category
• Total output length is appropriate for briefing type:
  • Daily: 1,500–2,500 words
  • Weekly: 3,000–5,000 words
  • Monthly: 5,000–10,000 words

Step 5.3: Add Contextual Notes (Optional)

If the user requested analysis or trends, append:

• Trend Spotlight: 2–3 emerging patterns observed this period
• Company Momentum: Which companies/labs are most active
• Technology Shifts: Notable changes in technical approaches
• Geographic Insights: Regional differences (e.g., US vs China ecosystem)

Use taxonomy.md → Section "5. Trend Analysis Framework" for guidance.

Phase 6: Delivery & Follow-up

1. Deliver the briefing in the selected format
2. Offer follow-up options:
   • "Would you like me to deep-dive into any specific story?"
   • "Should I track these companies/topics for your next briefing?"
   • "Would you like a comparison with last week/month's trends?"