Reinforcement Learning for Robotic Manipulation using Simulated Locomotion Demonstrations

TL;DR

This paper introduces a novel reinforcement learning framework for robotic manipulation that leverages simulated object locomotion demonstrations to improve learning efficiency without human demonstrations.

Contribution

The authors propose using simulated object locomotion policies to generate auxiliary rewards, enabling effective RL for manipulation tasks without human demonstrations.

Findings

Achieves higher success rates across 13 tasks

Faster learning compared to alternative algorithms

Particularly effective for multi-object stacking and non-rigid objects

Abstract

Mastering robotic manipulation skills through reinforcement learning (RL) typically requires the design of shaped reward functions. Recent developments in this area have demonstrated that using sparse rewards, i.e. rewarding the agent only when the task has been successfully completed, can lead to better policies. However, state-action space exploration is more difficult in this case. Recent RL approaches to learning with sparse rewards have leveraged high-quality human demonstrations for the task, but these can be costly, time consuming or even impossible to obtain. In this paper, we propose a novel and effective approach that does not require human demonstrations. We observe that every robotic manipulation task could be seen as involving a locomotion task from the perspective of the object being manipulated, i.e. the object could learn how to reach a target state on its own. In order to exploit this idea, we introduce a framework whereby an object locomotion policy is initially obtained using a realistic physics simulator. This policy is then used to generate auxiliary rewards, called simulated locomotion demonstration rewards (SLDRs), which enable us to learn the robot manipulation policy. The proposed approach has been evaluated on 13 tasks of increasing complexity, and can achieve higher success rate and faster learning rates compared to alternative algorithms. SLDRs are especially beneficial for tasks like multi-object stacking and non-rigid object manipulation.