Back-stepping Experience Replay with Application to Model-free Reinforcement Learning for a Soft Snake Robot

TL;DR

This paper introduces Back-stepping Experience Replay (BER), a novel method to improve learning efficiency in model-free reinforcement learning for soft robots by constructing reversed trajectories, demonstrated on a soft snake robot with significant speed improvements.

Contribution

The paper presents BER, a new off-policy RL technique compatible with arbitrary algorithms, tailored for systems with approximate reversibility, applied successfully to a soft snake robot.

Findings

Achieved 100% success rate in target reaching tasks.

Robot's average speed was 48% faster than baseline methods.

BER effectively enhances learning efficiency in soft robot locomotion.

Abstract

In this paper, we propose a novel technique, Back-stepping Experience Replay (BER), that is compatible with arbitrary off-policy reinforcement learning (RL) algorithms. BER aims to enhance learning efficiency in systems with approximate reversibility, reducing the need for complex reward shaping. The method constructs reversed trajectories using back-stepping transitions to reach random or fixed targets. Interpretable as a bi-directional approach, BER addresses inaccuracies in back-stepping transitions through a distillation of the replay experience during learning. Given the intricate nature of soft robots and their complex interactions with environments, we present an application of BER in a model-free RL approach for the locomotion and navigation of a soft snake robot, which is capable of serpentine motion enabled by anisotropic friction between the body and ground. In addition, a dynamic simulator is developed to assess the effectiveness and efficiency of the BER algorithm, in which the robot demonstrates successful learning (reaching a 100% success rate) and adeptly reaches random targets, achieving an average speed 48% faster than that of the best baseline approach.