RRepoGEO

REPOGEO REPORT · LITE

thu-ml/RoboticsDiffusionTransformer

Default branch main · commit cd79363a · scanned 5/19/2026, 6:13:16 PM

GitHub: 1,707 stars · 156 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)

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

AI VISIBILITY SCORE
22 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 warn · 0 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 thu-ml/RoboticsDiffusionTransformer, 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
  • highhomepage#1
    Add the project page URL as the repository homepage

    Why:

    COPY-PASTE FIX
    https://your-project-page-url.com
  • mediumreadme#2
    Add a 'Comparison with Alternatives' section to the README

    Why:

    COPY-PASTE FIX
    ## Comparison with Alternatives

RDT-1B differentiates itself from other prominent robotics foundation models like RT-1, RT-2, Diffusion Policy, and GATO by being the *largest* (1B parameters) diffusion transformer pre-trained on the *largest* multi-robot dataset (1M+ episodes) specifically for bimanual manipulation. While others focus on single-arm or generalist tasks, RDT-1B excels in dexterity, zero-shot generalizability, and few-shot learning for complex dual-arm operations, making it uniquely suited for advanced mobile manipulators.

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 thu-ml/RoboticsDiffusionTransformer
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
ROS
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. ROS · recommended 1×
  2. MoveIt · recommended 1×
  3. Gazebo · recommended 1×
  4. PyTorch · recommended 1×
  5. TensorFlow · recommended 1×
  • CATEGORY QUERY
    How to implement an AI model for bimanual robot manipulation using vision and language?
    you: not recommended
    AI recommended (in order):
    1. ROS
    2. MoveIt
    3. Gazebo
    4. PyTorch
    5. TensorFlow
    6. Hugging Face Transformers
    7. OpenCV
    8. RLlib
    9. Acme
    10. dm_control

    AI recommended 10 alternatives but never named thu-ml/RoboticsDiffusionTransformer. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best foundation models for generalizable multi-robot imitation learning tasks?
    you: not recommended
    AI recommended (in order):
    1. RT-1 (Robotics Transformer 1)
    2. RT-2 (Robotics Transformer 2)
    3. Open-X Embodiment Datasets
    4. Diffusion Policy
    5. GATO (Generalist Agent for Transferable Operations)
    6. Perceiver IO

    AI recommended 6 alternatives but never named thu-ml/RoboticsDiffusionTransformer. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    warn

    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 thu-ml/RoboticsDiffusionTransformer?
    pass
    AI did not name thu-ml/RoboticsDiffusionTransformer — 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 thu-ml/RoboticsDiffusionTransformer in production, what risks or prerequisites should they evaluate first?
    pass
    AI named thu-ml/RoboticsDiffusionTransformer 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 thu-ml/RoboticsDiffusionTransformer solve, and who is the primary audience?
    pass
    AI did not name thu-ml/RoboticsDiffusionTransformer — 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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thu-ml/RoboticsDiffusionTransformer — 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