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Workshop: Deep Reinforcement Learning

Transferring Dexterous Manipulation from GPU Simulation to a Remote Real-World Trifinger

Arthur Allshire · Mayank Mittal · Varun Lodaya · Viktor Makoviychuk · Denys Makoviichuk · Felix Widmaier · Manuel Wuethrich · Stefan Bauer · Ankur Handa · Animesh Garg


We present a system for learning a challenging dexterous manipulation task involving moving a cube to an arbitrary 6-DoF pose with only 3-fingers trained with NVIDIA's IsaacGym simulator. We show empirical benefits, both in simulation and sim-to-real transfer, of using keypoints as opposed to position+quaternion representations for the object pose in 6-DoF for policy observations and in reward calculation to train a model-free reinforcement learning agent. By utilizing domain randomization strategies along with the keypoint representation of the pose of the manipulated object, we achieve a high success rate of 83\% on a remote TriFinger system maintained by the organizers of the Real Robot Challenge. With the aim of assisting further research in learning in-hand manipulation, we make the codebase of our system, along with trained checkpoints that come with billions of steps of experience available, at \url{}

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