RRepoGEO

REPOGEO REPORT · LITE

NVlabs/GraspGen

Default branch main · commit a56d518f · scanned 6/30/2026, 10:27:27 PM

GitHub: 503 stars · 71 forks

AI VISIBILITY SCORE
40 /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
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 NVlabs/GraspGen, 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
    Expand repository topics with specific keywords

    Why:

    CURRENT
    robotics
    COPY-PASTE FIX
    robotics, 6-dof-grasping, grasp-generation, diffusion-models, robotic-manipulation, computer-vision, point-cloud-processing, gripper-control
  • mediumreadme#2
    Emphasize unique differentiators in the README's opening

    Why:

    CURRENT
    GraspGen is a modular framework for diffusion-based 6-DOF robotic grasp generation that scales across diverse settings: 1) **embodimentswith 3 distinct gripper types (industrial pinch gripper, suction) 2) **observabilityrobustness to partial vs. complete 3D point clouds and 3) **complexitygrasping single-object vs. clutter. We also introduce a novel and performant on-generator training recipe for the grasp discriminator, which scores and ranks the generated grasps. GraspGen outperforms prior methods in real and sim (SOTA performance on the FetchBench grasping benchmark, 17% improvement) while being performant (21X less memory) and realtime (20 Hz before TensorRT). We release the data generation, data formats as well as the training and inference infrastructure in this repo.
    COPY-PASTE FIX
    GraspGen is a modular framework for **diffusion-based 6-DOF robotic grasp generation, uniquely trained without explicit grasp labels**, that scales across diverse settings: 1) **embodiments** with 3 distinct gripper types (industrial pinch gripper, suction), 2) **observability** robustness to partial vs. complete 3D point clouds, and 3) **complexity** grasping single-object vs. clutter. We also introduce a novel and performant on-generator training recipe for the grasp discriminator, which scores and ranks the generated grasps. GraspGen outperforms prior methods in real and sim (SOTA performance on the FetchBench grasping benchmark, 17% improvement) while being performant (21X less memory) and realtime (20 Hz before TensorRT). We release the data generation, data formats as well as the training and inference infrastructure in this repo.
  • mediumlicense#3
    Clarify the project's license in the README

    Why:

    COPY-PASTE FIX
    ## License
This project is released under [specify license(s) here, e.g., a custom NVIDIA license or a combination of licenses]. Please refer to the `LICENSE` file for full details.

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 NVlabs/GraspGen
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
MoveIt!
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. MoveIt! · recommended 2×
  2. GraspNet-1B / GraspNet API · recommended 1×
  3. Dex-Net · recommended 1×
  4. VPGNet · recommended 1×
  5. Grasp-Anything · recommended 1×
  • CATEGORY QUERY
    How can I generate robust 6-DOF robotic grasps for objects in cluttered scenes?
    you: not recommended
    AI recommended (in order):
    1. GraspNet-1B / GraspNet API
    2. Dex-Net
    3. VPGNet
    4. Grasp-Anything
    5. Robotic Grasping Toolbox (MATLAB)
    6. MoveIt!
    7. OpenRAVE
    8. QT-Opt
    9. Transporter Networks

    AI recommended 9 alternatives but never named NVlabs/GraspGen. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools help generate robotic grasps from incomplete 3D sensor data for different grippers?
    you: not recommended
    AI recommended (in order):
    1. GraspIt!
    2. Grasping Research at Columbia (GRASP) Library
    3. OpenGRASP
    4. PyTorch
    5. TensorFlow
    6. PointNetGPD
    7. Contact-GraspNet
    8. GraspNet-1Billion
    9. MoveIt!
    10. Robotiq Grasping Library
    11. V-REP
    12. CoppeliaSim

    AI recommended 12 alternatives but never named NVlabs/GraspGen. 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 NVlabs/GraspGen?
    pass
    AI named NVlabs/GraspGen explicitly

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

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

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

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NVlabs/GraspGen — 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