Скачать или смотреть Reinforcement Learning Enables Real-Time Planning and Control of Agile Maneuvers for Soft Robot Arms

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Control policies for soft robot arms typically assume quasi-static motion or require a hand-designed motion plan. To achieve real-time planning and control for tasks requiring highly dynamic maneuvers, we apply deep reinforcement learning to train a policy entirely in simulation, and we identify strategies and insights that bridge the gap between simulation and reality. In particular, we strengthen the policy’s tolerance for inaccuracies with domain randomization and implement crucial simulator modifications that improve actuation and sensor modeling, enabling zero-shot sim-to-real transfer without requiring high-fidelity soft robot dynamics. We demonstrate the effectiveness of this approach with experiments on physical hardware and show that our soft robot can reach target positions that require dynamic swinging motions. This is the first work to achieve such agile maneuvers on a physical soft robot, advancing the field of soft robot arm planning and control. Our code and videos are publicly available at https://sites.google.com/view/rl-soft-robot.