魚型ロボットの経路追従のための微分可能強化学習
Differentiable Reinforcement Learning for Path Tracking by an Agile Fish-Like Robot
魚型ロボットの運動制御と経路追従を、計算効率の良いシミュレーション環境とPID制御のゲインを学習する微分可能強化学習を用いて実現し、実機で検証した。
著者: Prashanth Chivkula, Kartik Loya, Venkata Ravindhra Reddy Varikuti, Phanindra Tallapragada
分類: cs.RO, eess.SY
原文アブストラクト
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.