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what is graspnet-baseline fr?

peng-zhihui/graspnet-baseline — explained in plain English

Analysis updated 2026-07-18 · repo last pushed 2021-06-24

★ 83Audience · researcherComplexity · 4/5DormantSetup · hard

tl;dr

GraspNet Baseline is a machine learning model that looks at 3D camera images of objects and figures out the best places for a robot hand to grip each one without dropping it.

vibe map

mindmap
  root((graspnet-baseline))
    What it does
      Analyzes depth images
      Scores grasp points
      Filters unsafe grips
    Use Cases
      Warehouse automation
      Bin-picking systems
      Robotic arm manufacturing
    Audience
      Roboticists
      Researchers
    Tech Stack
      RealSense
      Kinect
      Pretrained weights

Code map

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filefunction / class

what do people make with this?

VIBE 1

Use the pretrained RealSense or Kinect model to generate ranked grasp points for objects in a bin-picking robot setup.

VIBE 2

Fine-tune the model on your own depth camera data to improve grip accuracy for a specific set of objects.

VIBE 3

Train the model from scratch on the large standardized dataset of over a billion grasp annotations.

VIBE 4

Filter out unsafe grasp candidates that would collide with the table or nearby objects before executing a pick.

what's the stack?

RealSenseKinect

how it stacks up fr

peng-zhihui/graspnet-baselineanvia-hq/lexacognivo-future-technologies-cft/awardx
Stars838383
Language—RustTypeScript
Last pushed2021-06-24——
MaintenanceDormant——
Setup difficultyhardeasymoderate
Complexity4/52/54/5
Audienceresearcherdeveloperpm founder

Figures from each repo's GitHub metadata at analysis time.

how do i run it?

Difficulty · hard time til it works · 1day+

Requires a depth camera (RealSense or Kinect) and a large dataset or pretrained weights for meaningful results.

No license information was stated in the explanation.

prompts (copy fr)

prompt 1
I have a RealSense camera set up on a warehouse picking robot. Show me how to use GraspNet Baseline's pretrained weights to generate grasp points for objects on a shelf.
prompt 2
Explain how GraspNet Baseline scores and filters grasp candidates so I understand why some grips are ranked higher than others.
prompt 3
I want to fine-tune GraspNet Baseline on my own depth images instead of using the pretrained RealSense or Kinect weights. Walk me through the training pipeline.
prompt 4
Show me how to run the demo script in GraspNet Baseline on a single depth and color image pair so I can test grasp detection before deploying it on a real robot.

Frequently asked questions

what is graspnet-baseline fr?

GraspNet Baseline is a machine learning model that looks at 3D camera images of objects and figures out the best places for a robot hand to grip each one without dropping it.

Is graspnet-baseline actively maintained?

Dormant — no commits in 2+ years (last push 2021-06-24).

What license does graspnet-baseline use?

No license information was stated in the explanation.

How hard is graspnet-baseline to set up?

Setup difficulty is rated hard, with roughly 1day+ to a first successful run.

Who is graspnet-baseline for?

Mainly researcher.

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