RRepoGEO

REPOGEO REPORT · LITE

robotics-survey/Awesome-Robotics-Foundation-Models

Default branch main · commit 58ebfc2a · scanned 5/25/2026, 5:43:00 AM

GitHub: 1,376 stars · 120 forks

AI VISIBILITY SCORE
17 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 fail
Objective metadata checks
AI knows your name
1 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface robotics-survey/Awesome-Robotics-Foundation-Models, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highabout#1
    Add a concise repository description

    Why:

    COPY-PASTE FIX
    A curated list of papers and resources for Foundation Models in Robotics, serving as a partner repository for the survey paper 'Foundation Models in Robotics: Applications, Challenges, and the Future'.
  • mediumreadme#2
    Reinforce 'Awesome List' nature in README's opening sentence

    Why:

    CURRENT
    This is the partner repository for the survey paper "Foundation Models in Robotics: Applications, Challenges, and the Future". The authors hope this repository can act as a quick reference for roboticists who wish to read the relevant papers and implement the associated methods.
    COPY-PASTE FIX
    This is an Awesome List and partner repository for the survey paper "Foundation Models in Robotics: Applications, Challenges, and the Future". It serves as a curated quick reference for roboticists to explore relevant papers and associated methods.

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface robotics-survey/Awesome-Robotics-Foundation-Models
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
RT-1 (Robotics Transformer 1)
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. RT-1 (Robotics Transformer 1) · recommended 2×
  2. RT-2 (Robotics Transformer 2) · recommended 2×
  3. SayCan · recommended 1×
  4. PaLM-E · recommended 1×
  5. Code as Policies · recommended 1×
  • CATEGORY QUERY
    What are the latest advancements in applying large language models for robotic control?
    you: not recommended
    AI recommended (in order):
    1. SayCan
    2. PaLM-E
    3. Code as Policies
    4. RT-1 (Robotics Transformer 1)
    5. RT-2 (Robotics Transformer 2)
    6. Open-Vocabulary Mobile Manipulation (OVMM)
    7. CLIP
    8. ChatGPT/GPT-4
    9. ROS (Robot Operating System)
    10. Diffusion Policy
    11. RoboCat

    AI recommended 11 alternatives but never named robotics-survey/Awesome-Robotics-Foundation-Models. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Where can I find resources on foundation models for embodied AI and robot transformers?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers Library
    2. Hugging Face Hub
    3. Robotics Foundation Models (RFM) Project
    4. RT-1 (Robotics Transformer 1)
    5. RT-2 (Robotics Transformer 2)
    6. OpenAI's CLIP
    7. DALL-E 2
    8. DALL-E 3
    9. ImageBind
    10. Segment Anything Model (SAM)
    11. Mobile ALOHA Project
    12. PyTorch
    13. PyTorch Lightning

    AI recommended 13 alternatives but never named robotics-survey/Awesome-Robotics-Foundation-Models. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    fail

    Suggestion:

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of robotics-survey/Awesome-Robotics-Foundation-Models?
    pass
    AI did not name robotics-survey/Awesome-Robotics-Foundation-Models — likely talking about a different project

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts robotics-survey/Awesome-Robotics-Foundation-Models in production, what risks or prerequisites should they evaluate first?
    pass
    AI named robotics-survey/Awesome-Robotics-Foundation-Models explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo robotics-survey/Awesome-Robotics-Foundation-Models solve, and who is the primary audience?
    pass
    AI did not name robotics-survey/Awesome-Robotics-Foundation-Models — likely talking about a different project

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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