Shypd.aiThe largest open index of AI toolsdrl_grasping is an Open Source AI Agents & Automation tool that applies deep reinforcement learning to robotic grasping. It enables robots to grasp diverse objects from 3D octree observations and supports sim-to-real transfer.
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drl_grasping is an open-source project focused on advancing robotic manipulation through deep reinforcement learning. It enables robots to acquire robust grasping policies for diverse objects using compact 3D observations in the form of octrees. The project emphasizes sim-to-real transfer, allowing policies trained in simulation to be evaluated on real robots with zero-shot transfer. It includes multiple RL environments for robotic manipulation, supporting continuous actions in Cartesian space and various observation variants like RGB images, depth maps, and octrees. The framework is compatible with Gym API and has been tested with end-to-end model-free actor-critic algorithms like TD3, SAC, and TQC, with a setup for model-based algorithms also provided.
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