RRepoGEO

REPOGEO REPORT · LITE

FlagOpen/Robo-Dopamine

Default branch main · commit 2c714abc · scanned 6/8/2026, 5:31:56 AM

GitHub: 622 stars · 63 forks

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 FlagOpen/Robo-Dopamine, 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
  • highreadme#1
    Add a concise introductory paragraph to the README

    Why:

    CURRENT
    The current README structure places the news section directly after the title and link block.
    COPY-PASTE FIX
    Insert the following paragraph immediately after the `h3` tag and before the `<p align="center">` block of links:

```
Robo-Dopamine introduces a novel framework for General Process Reward Modeling (GRM) specifically engineered to achieve high-precision robotic manipulation. This project provides the official implementation for our CVPR 2026 paper, demonstrating how GRM can significantly enhance robotic learning and control in complex tasks.
```
  • mediumreadme#2
    Elaborate on 'General Process Reward Modeling' in the README

    Why:

    COPY-PASTE FIX
    Add a dedicated section or expand an existing one in the README to explicitly define and elaborate on 'General Process Reward Modeling,' explaining its principles, how Robo-Dopamine implements it, and its advantages for high-precision robotic manipulation.

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 FlagOpen/Robo-Dopamine
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
DLR-RM/stable-baselines3
Recommended in 3 of 2 queries
COMPETITOR LEADERBOARD
  1. DLR-RM/stable-baselines3 · recommended 3×
  2. ray-project/ray · recommended 3×
  3. pytorch/pytorch · recommended 3×
  4. scipy/scipy · recommended 2×
  5. tensorflow/tensorflow · recommended 2×
  • CATEGORY QUERY
    Seeking frameworks for high-precision robotic manipulation through process reward modeling.
    you: not recommended
    AI recommended (in order):
    1. RLBench
    2. PyBullet
    3. Isaac Gym
    4. RoboStack
    5. ROS (Robot Operating System)
    6. Gazebo
    7. MoveIt
    8. MuJoCo
    9. Gymnasium

    AI recommended 9 alternatives but never named FlagOpen/Robo-Dopamine. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best methods for general process reward modeling in robotics?
    you: not recommended
    AI recommended (in order):
    1. trl (huggingface/trl)
    2. InstructBLIP
    3. Flamingo
    4. RewardBench (openrlbenchmark/RewardBench)
    5. Stable Baselines3 (DLR-RM/stable-baselines3)
    6. RLlib (ray-project/ray)
    7. SciPy (scipy/scipy)
    8. PyTorch (pytorch/pytorch)
    9. PyBullet (bulletphysics/bullet3)
    10. MuJoCo (deepmind/mujoco)
    11. PyTorch (pytorch/pytorch)
    12. TensorFlow (tensorflow/tensorflow)
    13. Stable Baselines3 (DLR-RM/stable-baselines3)
    14. RLlib (ray-project/ray)
    15. SURF
    16. RCPs
    17. GPyOpt (SheffieldML/GPyOpt)
    18. BoTorch (pytorch/botorch)
    19. DEAP (deap/deap)
    20. PyGAD (ahmedfgad/PyGAD)
    21. NumPy (numpy/numpy)
    22. SciPy (scipy/scipy)
    23. Stable Baselines3 (DLR-RM/stable-baselines3)
    24. RLlib (ray-project/ray)
    25. PyTorch (pytorch/pytorch)
    26. TensorFlow (tensorflow/tensorflow)

    AI recommended 26 alternatives but never named FlagOpen/Robo-Dopamine. 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 FlagOpen/Robo-Dopamine?
    pass
    AI named FlagOpen/Robo-Dopamine explicitly

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

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

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

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FlagOpen/Robo-Dopamine — 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