RRepoGEO

REPOGEO REPORT · LITE

octo-models/octo

Default branch main · commit 241fb351 · scanned 5/20/2026, 12:47:01 AM

GitHub: 1,654 stars · 270 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
35 /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
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 octo-models/octo, 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 for robotics and AI

    Why:

    COPY-PASTE FIX
    robotics, transformer, diffusion-policy, generalist-robot-policy, robot-learning, machine-learning, ai, deep-learning, robot-control
  • highreadme#2
    Reposition the README's opening sentence to clearly state Octo's core identity

    Why:

    CURRENT
    This repo contains code for training and finetuning Octo generalist robotic policies (GRPs). Octo models are transformer-based diffusion policies, trained on a diverse mix of 800k robot trajectories.
    COPY-PASTE FIX
    Octo is a **generalist robotic policy (GRP)**, a transformer-based diffusion model trained on a diverse mix of 800k robot trajectories. This repository provides the code for training and finetuning these policies.
  • mediumreadme#3
    Emphasize Octo's unique generalist capability and data scale early in the README

    Why:

    COPY-PASTE FIX
    Leveraging a massive and diverse dataset of 800k robot trajectories, Octo stands out as a highly adaptable solution for controlling various robot arms via language or goal images.

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 octo-models/octo
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. Perceiver IO · recommended 2×
  3. RT-2 · recommended 2×
  4. RRT-Diffusion · recommended 1×
  5. Gato · recommended 1×
  • CATEGORY QUERY
    How to train a generalist robot policy using diverse trajectory datasets?
    you: not recommended
    AI recommended (in order):
    1. Diffusion Policy
    2. RRT-Diffusion
    3. Perceiver IO
    4. Gato
    5. Robotics Transformer
    6. RT-1
    7. RT-2
    8. Large Language Models (LLMs)
    9. Vision-Language Models (VLMs)
    10. CLIP
    11. LLaVA
    12. Conservative Q-Learning (CQL)
    13. Implicit Q-Learning (IQL)
    14. TD3+BC
    15. MT-Opt
    16. ALOHA
    17. RoboSet
    18. Isaac Sim

    AI recommended 18 alternatives but never named octo-models/octo. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are good transformer-based models for controlling robot arms with language commands?
    you: not recommended
    AI recommended (in order):
    1. RT-2
    2. CLIPort
    3. SayCan
    4. RoboCat
    5. Perceiver IO
    6. Diffusion Policy

    AI recommended 6 alternatives but never named octo-models/octo. 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 octo-models/octo?
    pass
    AI named octo-models/octo explicitly

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

  • If a team adopts octo-models/octo in production, what risks or prerequisites should they evaluate first?
    pass
    AI named octo-models/octo 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 octo-models/octo solve, and who is the primary audience?
    pass
    AI named octo-models/octo explicitly

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

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octo-models/octo — RepoGEO report