andyzeng/visual-pushing-grasping

Health v2 · maintenance only

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Adoption not part of the score

1109 stars · 328 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

PyTorch reference implementation of Visual Pushing and Grasping (VPG), which trains robotic agents via self-supervised deep reinforcement learning to plan complementary pushing and grasping actions from RGB-D observations. It supports training and testing in simulation and on a real UR5 robot arm, based on the IROS 2018 paper.

Use cases

• train a robot to grasp objects in cluttered scenes
• learn pushing and grasping policies with deep reinforcement learning
• run robotic manipulation experiments in simulation and on a real UR5 arm
• reproduce results from the visual pushing and grasping paper
• train self-supervised manipulation policies from RGB-D images
• research synergies between non-prehensile and prehensile robot actions

When to choose

• you need a proven reference implementation of deep RL for robotic pushing and grasping
• you want to train manipulation policies from RGB-D visual observations with self-supervision
• you are doing research on robotic manipulation and want a well-cited baseline

When to avoid

• you need a production-ready or actively maintained robotics framework
• you use robot hardware other than a UR5 arm without adaptation work
• you need general-purpose robot simulation tooling rather than this specific VPG method

Facets

library · maturity maintenance

machine-learning deep-learning reinforcement-learning computer-vision robotics robotics reinforcement-learning computer-vision artificial-intelligence python robot-manipulation grasping pushing pytorch q-learning self-supervised-learning rgb-d research-code ur5-robot-arm linux gpu

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