Shypd.aiThe largest open index of AI toolsVisual Pushing and Grasping (VPG) is an AI framework that trains robotic agents to plan complementary pushing and grasping actions using deep reinforcement learning. It operates directly on visual observations and learns from trial and error in both simulation and real-world settings.
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Visual Pushing and Grasping (VPG) is a method for training robotic agents to learn how to plan complementary pushing and grasping actions for manipulation, particularly useful in unstructured pick-and-place applications. This framework operates directly on visual observations, utilizing RGB-D images, and learns through a process of trial and error. It trains quickly and demonstrates generalization to new objects and scenarios. The provided repository offers PyTorch code for training and testing VPG policies with deep reinforcement learning in both simulation and real-world environments, specifically on a UR5 robot arm. The system is designed to discover and learn synergies between non-prehensile (pushing) and prehensile (grasping) actions from scratch, using two fully convolutional networks trained jointly in a Q-learning framework.
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