RRepoGEO

REPOGEO REPORT · LITE

OpenDriveLab/WholebodyVLA

Default branch main · commit 7a86f5cb · scanned 6/27/2026, 11:58:20 PM

GitHub: 501 stars · 14 forks

AI VISIBILITY SCORE
33 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 warn · 0 fail
Objective metadata checks
AI knows your name
2 / 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 OpenDriveLab/WholebodyVLA, 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

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

OVERALL DIRECTION
  • highreadme#1
    Reposition core project definition in README

    Why:

    CURRENT
    The "Overview" section starts with "WholeBodyVLA is a unified Vision-Language-Action framework for large-space humanoid loco-manipulation." This sentence appears after the main title, author list, and "Highlights" section.
    COPY-PASTE FIX
    Add the following sentence immediately after the main title: "WholeBodyVLA is a unified Vision-Language-Action framework for large-space humanoid loco-manipulation."
  • mediumcomparison#2
    Add a comparison/differentiators section to README

    Why:

    COPY-PASTE FIX
    Add a new section to the README, for example, `## 💡 Differentiators & Comparison`, that explains WholebodyVLA's unique focus on "unified, language-driven, whole-body control for humanoid robots," contrasting it with more general robotics frameworks or VLMs.
  • lowreadme#3
    Add an explicit target audience/use cases section

    Why:

    COPY-PASTE FIX
    Add a new section to the README, such as `## 🎯 Target Audience & Use Cases`, explicitly stating that WholeBodyVLA is primarily for researchers and developers working on advanced humanoid robot control, loco-manipulation, and vision-language-action systems, especially those dealing with unannotated video data for learning latent actions.

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 OpenDriveLab/WholebodyVLA
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
OpenAI Robotics Stack
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. OpenAI Robotics Stack · recommended 1×
  2. GPT-4V · recommended 1×
  3. ROS 2 · recommended 1×
  4. MoveIt 2 · recommended 1×
  5. Open3D · recommended 1×
  • CATEGORY QUERY
    Need a VLA framework for closed-loop humanoid robot loco-manipulation in large environments.
    you: not recommended
    AI recommended (in order):
    1. OpenAI Robotics Stack
    2. GPT-4V
    3. ROS 2
    4. MoveIt 2
    5. Open3D
    6. PCL
    7. Google DeepMind's RT-X
    8. RT-1
    9. RT-2
    10. PaLM-E
    11. Gemini
    12. LLaVA
    13. BLIP-2
    14. robot_localization
    15. Nav2
    16. Habitat 2.0
    17. PyRobot
    18. Isaac Sim
    19. NVIDIA Omniverse
    20. Isaac ROS

    AI recommended 20 alternatives but never named OpenDriveLab/WholebodyVLA. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools learn unified latent actions for robot control from unannotated video data?
    you: not recommended
    AI recommended (in order):
    1. Diffusion Policy
    2. Perceiver IO / Perceiver Actor
    3. Robotics Transformer (RT-1, RT-2)
    4. DreamerV3
    5. R3M (Robotics Reward Reshaping with Masked Autoencoders)
    6. Video PreTraining (VPT)

    AI recommended 6 alternatives but never named OpenDriveLab/WholebodyVLA. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    pass

  • 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 OpenDriveLab/WholebodyVLA?
    pass
    AI named OpenDriveLab/WholebodyVLA explicitly

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

  • If a team adopts OpenDriveLab/WholebodyVLA in production, what risks or prerequisites should they evaluate first?
    pass
    AI named OpenDriveLab/WholebodyVLA 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 OpenDriveLab/WholebodyVLA solve, and who is the primary audience?
    pass
    AI did not name OpenDriveLab/WholebodyVLA — 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?

Embed your GEO score

Drop this badge into the README of OpenDriveLab/WholebodyVLA. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/OpenDriveLab/WholebodyVLA.svg)](https://repogeo.com/en/r/OpenDriveLab/WholebodyVLA)
HTML
<a href="https://repogeo.com/en/r/OpenDriveLab/WholebodyVLA"><img src="https://repogeo.com/badge/OpenDriveLab/WholebodyVLA.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

OpenDriveLab/WholebodyVLA — 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