Realtime Planning for High-DOF Deformable Bodies Using Two-Stage Learning

Abstract

We present a method for planning the motion of arbitrarily-shaped volumetric deformable bodies or robots through complex environments. Such robots have very high-dimensional configuration spaces and we compute trajectories that satisfy the dynamics constraints using a two-stage learning method. First, we train a multitask controller parameterized using dynamic movement primitives (DMP), which encodes various locomotion or movement skills. Next, we train a neural-network controller to select the DMP task to navigate the robot through environments while avoiding obstacles. By combining the finite element method (FEM), model reduction, and contact invariant optimization (CIO), the DMP controller's parameters can be optimized efficiently using a gradient-based method, while the neural-network's parameters are optimized using Deep Q-Learning (DQL). This two-stage learning algorithm also allows us to reuse the trained DMP controller for different navigation tasks, such as moving through different environmental types and to different goal positions. Our results show that the learned motion planner can navigate swimming and walking deformable robots with thousands of DOFs at realtime.