用可微强化学习让仿鱼机器人精准跟踪路径。
Differentiable Reinforcement Learning for Path Tracking by an Agile Fish-Like Robot
- 通过可微仿真平台实现高效物理建模
- 基于时间反向传播学习动态PID参数
- 模拟训练结果直接适配真实机器人
仿鱼游泳已激发数十甚至上百种生物启发式机器人的设计。然而，由于流固耦合建模不充分以及此类机器人非线性欠驱动动力学的复杂性，其控制与运动规划仍具挑战。尽管强化学习在地面和空中机器人中取得显著进展，但缺乏兼具计算速度与精度的合适仿真环境，阻碍了其在仿鱼机器人中的应用。本文通过开发一个计算高效的仿真平台，近似作者所设计仿鱼机器人的运动特性。随后，采用PID控制实现机器人运动控制与路径跟踪，其中可变增益通过时间反向传播学习，并在课程学习框架下训练。该策略在仿真中训练后直接部署于物理平台，表现出极佳的匹配效果。
原文摘要 · Abstract (English)
Fish-like swimming has inspired the design of several dozens if not hundreds of bioinspired robots in the last few decades. But the control and motion planning of such robots has been challenging due to the poorly modeled fluid-structure interaction and the nonlinear underactuated dynamics of such robots. While reinforcement learning has allowed significant advances in the context of ground and aerial robots, the lack of a suitable simulation environment with appropriate computational speed and accuracy have prevented similar progress for fish-like robots. We address this two-fold problem by developing a simulation platform that approximates the motion of our fish-like robot with computational efficiency. Then the motion control and path tracking by the robot is performed using PID control where the (variable) gains are learned using back propagation through time and training on a curriculum. The policy learned in the simulation is then applied on the physical platform, demonstrating an excellent match.
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