Learning to Locomote with Deep Neural-Network and CPG-based Control in a Soft Snake Robot

no code implementations • 13 Jan 2020 • Xuan Liu, Renato Gasoto, Cagdas Onal, Jie Fu

Inspired by biological snakes, our control architecture is composed of two key modules: A deep reinforcement learning (RL) module for achieving adaptive goal-tracking behaviors with changing goals, and a central pattern generator (CPG) system with Matsuoka oscillators for generating stable and diverse locomotion patterns.

Reinforcement Learning (RL)