RRepoGEO

REPOGEO REPORT · LITE

TommyZihao/vlm_arm

Default branch main · commit 230a9bbc · scanned 5/13/2026, 9:48:34 PM

GitHub: 1,182 stars · 198 forks

AI VISIBILITY SCORE
30 /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
3 / 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 TommyZihao/vlm_arm, 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
  • highlicense#1
    Add a LICENSE file to clarify usage rights

    Why:

    COPY-PASTE FIX
    Create a `LICENSE` file in the repository root, for example, using the MIT License template.
  • mediumhomepage#2
    Add a project homepage URL

    Why:

    COPY-PASTE FIX
    https://www.bilibili.com/video/BV18w4m1U7Fi

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 TommyZihao/vlm_arm
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
MoveIt!
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. MoveIt! · recommended 2×
  2. OpenCV · recommended 2×
  3. TensorFlow · recommended 2×
  4. PyTorch · recommended 2×
  5. ROS · recommended 1×
  • CATEGORY QUERY
    How can I integrate multimodal AI with a robotic arm for human-robot interaction?
    you: not recommended
    AI recommended (in order):
    1. ROS
    2. MoveIt!
    3. RViz
    4. OpenCV
    5. Hugging Face Transformers
    6. ViLT
    7. BLIP
    8. CLIP
    9. DeepSpeech
    10. OpenAI Whisper
    11. Google MediaPipe
    12. TensorFlow
    13. PyTorch
    14. Gazebo
    15. NVIDIA Isaac Sim

    AI recommended 15 alternatives but never named TommyZihao/vlm_arm. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a framework to enable large language models to control a robotic arm via vision.
    you: not recommended
    AI recommended (in order):
    1. ROS (Robot Operating System)
    2. MoveIt!
    3. OpenCV
    4. RoboStack
    5. NVIDIA Isaac Gym
    6. Isaac Sim
    7. OpenVINO
    8. ROS 2
    9. PyRobot
    10. PyTorch
    11. TensorFlow
    12. Franka Emika Panda's Desk
    13. Franka Emika Panda's SDK
    14. URCaps

    AI recommended 14 alternatives but never named TommyZihao/vlm_arm. 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 TommyZihao/vlm_arm?
    pass
    AI named TommyZihao/vlm_arm explicitly

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

  • If a team adopts TommyZihao/vlm_arm in production, what risks or prerequisites should they evaluate first?
    pass
    AI named TommyZihao/vlm_arm 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 TommyZihao/vlm_arm solve, and who is the primary audience?
    pass
    AI named TommyZihao/vlm_arm explicitly

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

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TommyZihao/vlm_arm — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite