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GraspKit

Python 3.10+ GitHub Actions MIT License

GraspKit is an end-to-end toolkit for manipulation across various robot embodiments, including grippers and dexterous hands. It synthesizes grasps, plans trajectories, trains policies, and deploys them in simulation or on real robots; it is built on top of RoboKit.

Installation

Requires Python 3.10–3.14 (the full pipeline with ManiSkill and spconv: 3.10–3.12):

uv add graspkit      # or: pip install graspkit

See Installation for source installs, PyTorch options, and asset access.

Features

1,024 dexterous grasps optimized in one GPU batch
Fast grasp optimization
Object point cloud mapped by a grasp generator to a hand grasp
Policy learning
Different arms with dexterous hands grasping the same object in simulation
All arms, all hands
Two real xArm robots with dexterous hands, diagonally split with their aligned simulation twin
Real-world robustness

Explore robot presets and optimized grasps in the interactive gallery.

How it works

For each scene, GraspKit optimizes and plans grasps on the GPU and renders the camera's point cloud. A policy trained on these pairs maps point clouds to grasp trajectories, in simulation or on real robots.

Stage Role Read more
Grasp optimization Generate grasps, plan motions, and optionally validate them in simulation. Grasp optimization
Rendering Render point-cloud observations for the same scenes. Pipeline
Data processing Join observations and grasps into policy training data. Pipeline
Training Train a grasp policy on the processed data. Training
Deployment Run a trained policy in simulation or on a real robot. Sim, real

See the core overview for the full stage graph and artifacts.

Metadata

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