Autonomous Locomotion of a Rat Robot Based on Reinforcement Learning

Abstract

The rat robot is a soft compact quadrupedal robot with the same size as real rats. It is difficult for such robots to learn effective motions on complex terrain owing to their underactuated nature and limited sensors. This paper proposes a novel approach for the rat robot to learn adaptive motion on rugged terrain based on reinforcement learning. The training architecture is designed for the rat robot’s nonlinear control structure. We gather and analyze perception information based on changes in time slices to monitor environmental changes during robot walking. Our proposed framework demonstrates a significant reduction in training convergence time, from millions to hundreds of thousands, compared to commonly used reinforcement learning methods. We evaluate the efficacy of our approach on a varied set of simulated terrain scenarios, which include various obstacles and terrain undulations. Our results show that our approach effectively achieves efficient motions on complex terrains designed for small-sized robots.