RRepoGEO

REPOGEO REPORT · LITE

OpenDriveLab/UniVLA

Default branch main · commit 0ab9e9dd · scanned 6/27/2026, 9:03:55 AM

GitHub: 1,096 stars · 66 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

3 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
40 /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
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 OpenDriveLab/UniVLA, 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 README introduction to clarify primary application

    Why:

    CURRENT
    A recipe towards generalist policy by planning in a unified, embodiment-agnostic action space.
    COPY-PASTE FIX
    UniVLA is a novel Vision-Language-Action (VLA) model specifically designed for robust and generalizable autonomous driving, enabling a generalist policy by planning in a unified, embodiment-agnostic action space.
  • highabout#2
    Update repository description to include primary application

    Why:

    CURRENT
    [RSS 2025] Learning to Act Anywhere with Task-centric Latent Actions
    COPY-PASTE FIX
    [RSS 2025] UniVLA: A Vision-Language-Action model for generalist autonomous driving, learning to act anywhere with task-centric latent actions.
  • mediumtopics#3
    Add specific topics for autonomous driving and VLA

    Why:

    CURRENT
    robot-learning, vision-language-actions-models, vla
    COPY-PASTE FIX
    robot-learning, vision-language-actions-models, vla, autonomous-driving, generalist-robot-policy, embodied-ai

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/UniVLA
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Robotics Transformer (RT-1, RT-2)
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Robotics Transformer (RT-1, RT-2) · recommended 1×
  2. Diffusion Policy · recommended 1×
  3. Perceiver IO / Gato · recommended 1×
  4. DreamerV3 · recommended 1×
  5. MAML · recommended 1×
  • CATEGORY QUERY
    How to train a generalist robot policy that works across different physical embodiments?
    you: not recommended
    AI recommended (in order):
    1. Robotics Transformer (RT-1, RT-2)
    2. Diffusion Policy
    3. Perceiver IO / Gato
    4. DreamerV3
    5. MAML
    6. PEARL

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

    Show full AI answer
  • CATEGORY QUERY
    Looking for a VLA model for robot control that learns from diverse video data efficiently.
    you: not recommended
    AI recommended (in order):
    1. RT-X
    2. OpenVLA
    3. RT-2
    4. OCTO
    5. GATO
    6. SayCan

    AI recommended 6 alternatives but never named OpenDriveLab/UniVLA. 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/UniVLA?
    pass
    AI named OpenDriveLab/UniVLA 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/UniVLA in production, what risks or prerequisites should they evaluate first?
    pass
    AI named OpenDriveLab/UniVLA 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/UniVLA solve, and who is the primary audience?
    pass
    AI named OpenDriveLab/UniVLA explicitly

    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/UniVLA. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)
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HTML
<a href="https://repogeo.com/en/r/OpenDriveLab/UniVLA"><img src="https://repogeo.com/badge/OpenDriveLab/UniVLA.svg" alt="RepoGEO" /></a>
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OpenDriveLab/UniVLA — 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