Bi-Manual Block Assembly via Sim-to-Real Reinforcement Learning

• URL: http://arxiv.org/abs/2303.14870v1
• Date: Mon, 27 Mar 2023 01:25:24 GMT
• Title: Bi-Manual Block Assembly via Sim-to-Real Reinforcement Learning
• Authors: Satoshi Kataoka, Youngseog Chung, Seyed Kamyar Seyed Ghasemipour, Pannag Sanketi, Shixiang Shane Gu, Igor Mordatch
• Abstract summary: Two xArm6 robots solve the U-shape assembly task with a success rate of above90% in simulation, and 50% on real hardware without any additional real-world fine-tuning. Our results present a significant step forward for bi-arm capability on real hardware, and we hope our system can inspire future research on deep RL and Sim2Real transfer bi-manualpolicies.
• Score: 24.223788665601678
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Most successes in robotic manipulation have been restricted to single-arm gripper robots, whose low dexterity limits the range of solvable tasks to pick-and-place, inser-tion, and object rearrangement. More complex tasks such as assembly require dual and multi-arm platforms, but entail a suite of unique challenges such as bi-arm coordination and collision avoidance, robust grasping, and long-horizon planning. In this work we investigate the feasibility of training deep reinforcement learning (RL) policies in simulation and transferring them to the real world (Sim2Real) as a generic methodology for obtaining performant controllers for real-world bi-manual robotic manipulation tasks. As a testbed for bi-manual manipulation, we develop the U-Shape Magnetic BlockAssembly Task, wherein two robots with parallel grippers must connect 3 magnetic blocks to form a U-shape. Without manually-designed controller nor human demonstrations, we demonstrate that with careful Sim2Real considerations, our policies trained with RL in simulation enable two xArm6 robots to solve the U-shape assembly task with a success rate of above90% in simulation, and 50% on real hardware without any additional real-world fine-tuning. Through careful ablations,we highlight how each component of the system is critical for such simple and successful policy learning and transfer,including task specification, learning algorithm, direct joint-space control, behavior constraints, perception and actuation noises, action delays and action interpolation. Our results present a significant step forward for bi-arm capability on real hardware, and we hope our system can inspire future research on deep RL and Sim2Real transfer of bi-manualpolicies, drastically scaling up the capability of real-world robot manipulators.

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