REPOGEO REPORT · LITE

real-stanford/diffusion_policy

Default branch main · commit 5ba07ac6 · scanned 6/24/2026, 3:59:52 AM

GitHub: 4,302 stars · 791 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

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 real-stanford/diffusion_policy, 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

Explicitly state in the README's opening that this is the official implementation

Why:

CURRENT

# Diffusion Policy

COPY-PASTE FIX

# Diffusion Policy

This is the official open-source implementation of the Diffusion Policy paper: 'Diffusion Policy: Visuomotor Policy Learning via Action Diffusion' (RSS 2023).

hightopics#2

Add more specific topics to improve categorization

Why:

CURRENT

robotics

COPY-PASTE FIX

robotics, diffusion-models, visuomotor-control, imitation-learning, robot-learning, policy-learning, reinforcement-learning

mediumabout#3

Refine the repository description for clearer AI understanding

Why:

CURRENT

[RSS 2023] Diffusion Policy Visuomotor Policy Learning via Action Diffusion

COPY-PASTE FIX

Official implementation of Diffusion Policy: a framework for robust visuomotor policy learning in robotics using action diffusion models.

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 real-stanford/diffusion_policy

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

Diffusion Policy

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

Diffusion Policy · recommended 2×
Soft Actor-Critic (SAC) · recommended 1×
Proximal Policy Optimization (PPO) · recommended 1×
DreamerV3 · recommended 1×
Behavioral Cloning (BC) · recommended 1×

CATEGORY QUERY
What are effective methods for visuomotor control in robotic systems?
you: not recommended
AI recommended (in order):
1. Soft Actor-Critic (SAC)
2. Proximal Policy Optimization (PPO)
3. DreamerV3
4. Behavioral Cloning (BC)
5. Vision Transformer (ViT)
6. ResNet
7. Diffusion Policy
8. Iterative Linear Quadratic Regulator (iLQR)
9. Differential Dynamic Programming (DDP)
10. CasADi
11. ACADO
12. OSQP
13. Image-Based Visual Servoing (IBVS)
14. Position-Based Visual Servoing (PBVS)
AI recommended 14 alternatives but never named real-stanford/diffusion_policy. This is the gap to close.
Show full AI answer
CATEGORY QUERY
How to apply diffusion models for learning robot action sequences?
you: not recommended
AI recommended (in order):
1. Diffuser
2. PyTorch
3. TensorFlow
4. RoboDiffusion
5. Diffusion Policy
6. Stable Diffusion
7. Diffusers
8. OpenAI Gym
9. Gymnasium
10. RL-Diffusion
AI recommended 10 alternatives but never named real-stanford/diffusion_policy. 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 real-stanford/diffusion_policy?
pass
AI named real-stanford/diffusion_policy explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts real-stanford/diffusion_policy in production, what risks or prerequisites should they evaluate first?
pass
AI named real-stanford/diffusion_policy 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 real-stanford/diffusion_policy solve, and who is the primary audience?
pass
AI did not name real-stanford/diffusion_policy — 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

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MARKDOWN (README)

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HTML

<a href="https://repogeo.com/en/r/real-stanford/diffusion_policy"><img src="https://repogeo.com/badge/real-stanford/diffusion_policy.svg" alt="RepoGEO" /></a>

Pro

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real-stanford/diffusion_policy — 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