RRepoGEO

REPOGEO REPORT · LITE

thu-ml/RoboticsDiffusionTransformer

Default branch main · commit cd79363a · scanned 7/1/2026, 3:37:53 AM

GitHub: 1,735 stars · 161 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
28 /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
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 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

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

OVERALL DIRECTION
  • hightopics#1
    Add specific topics to improve categorization

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    robotics, diffusion-models, transformers, robot-manipulation, bimanual-robotics, foundation-models, imitation-learning, pytorch
  • mediumcomparison#2
    Add a 'Comparison to Alternatives' section in README

    Why:

    COPY-PASTE FIX
    ## Comparison to Alternatives

RDT-1B differentiates itself from other robotics foundation models and diffusion policies by being the largest (1B parameters) and most extensively pre-trained (1M+ multi-robot episodes) Diffusion Transformer for bimanual manipulation. Unlike models focused on single-arm or specific robot types, RDT-1B is inherently compatible with almost all modern mobile manipulators, offering state-of-the-art dexterity, zero-shot generalizability, and few-shot learning capabilities, particularly demonstrated on dual-arm systems like ALOHA.
  • lowabout#3
    Enhance the repository description with key capabilities

    Why:

    CURRENT
    RDT-1B: a Diffusion Foundation Model for Bimanual Manipulation
    COPY-PASTE FIX
    RDT-1B: a Diffusion Foundation Model for generalizable bimanual robot action prediction across diverse multi-robot manipulation tasks.

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
Diffusion Policy
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Diffusion Policy · recommended 2×
  2. OpenAI GPT-4V (Vision) · recommended 1×
  3. Google DeepMind's RT-X (Robotics Transformer Family) · recommended 1×
  4. Meta AI's DINOv2 (Vision Transformer for Self-Supervised Learning) · recommended 1×
  5. Hugging Face Transformers · recommended 1×
  • CATEGORY QUERY
    How to use a foundation model for bimanual robot action prediction?
    you: not recommended
    AI recommended (in order):
    1. OpenAI GPT-4V (Vision)
    2. Google DeepMind's RT-X (Robotics Transformer Family)
    3. Meta AI's DINOv2 (Vision Transformer for Self-Supervised Learning)
    4. Hugging Face Transformers
    5. Diffusion Policy

    AI recommended 5 alternatives but never named thu-ml/RoboticsDiffusionTransformer. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best diffusion models for generalizable multi-robot manipulation tasks?
    you: not recommended
    AI recommended (in order):
    1. Diffusion Policy
    2. RoboDiffusion
    3. Actuator-aware Diffusion Policies
    4. R3M
    5. Perceiver-Actor

    AI recommended 5 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 named thu-ml/RoboticsDiffusionTransformer explicitly

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

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  • Brand-free category queries5 vs 2 in Lite
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