Reinforcement Learning for Robotic Safe Control with Force Sensing

TL;DR

This paper introduces a force-based reinforcement learning approach that enhances robotic safety and adaptability, especially in unstructured environments and sim-to-real transfer, demonstrated through object pushing tasks.

Contribution

The paper integrates force and tactile perception into reinforcement learning to improve safety, reliability, and transferability of robotic control in complex environments.

Findings

Force-based RL improves safety in robotic manipulation.

Enhanced sim-to-real transfer with force sensing.

Better adaptability in unstructured environments.

Abstract

For the task with complicated manipulation in unstructured environments, traditional hand-coded methods are ineffective, while reinforcement learning can provide more general and useful policy. Although the reinforcement learning is able to obtain impressive results, its stability and reliability is hard to guarantee, which would cause the potential safety threats. Besides, the transfer from simulation to real world also will lead in unpredictable situations. To enhance the safety and reliability of robots, we introduce the force and haptic perception into reinforcement learning. Force and tactual sensation play key roles in robotic dynamic control and human-robot interaction. We demonstrate that the force-based reinforcement learning method can be more adaptive to environment, especially in sim-to-real transfer. Experimental results show in object pushing task, our strategy is safer and more efficient in both simulation and real world, thus it holds prospects for a wide variety of robotic applications.