DSL-Assembly: A Robust and Safe Assembly Strategy

TL;DR

This paper introduces a reinforcement learning-based assembly strategy for robots that uses force feedback and a safety lock mechanism to improve safety and success rates in assembly tasks, validated through simulation and real-world tests.

Contribution

It presents a novel force-based safety lock (DSL) and demonstrates its effectiveness in robotic assembly tasks using reinforcement learning, with successful transfer from simulation to real-world deployment.

Findings

88.57% success rate in simulation with 4mm gap

79.63% success rate on real robot with 4mm gap

Effective force-based safety mechanism enhances assembly safety

Abstract

A reinforcement learning (RL) based method that enables the robot to accomplish the assembly-type task with safety regulations is proposed. The overall strategy consists of grasping and assembly, and this paper mainly considers the assembly strategy. Force feedback is used instead of visual feedback to perceive the shape and direction of the hole in this paper. Furthermore, since the emergency stop is triggered when the force output is too large, a force-based dynamic safety lock (DSL) is proposed to limit the pressing force of the robot. Finally, we train and test the robot model with a simulator and build ablation experiments to illustrate the effectiveness of our method. The models are independently tested 500 times in the simulator, and we get an 88.57% success rate with a 4mm gap. These models are transferred to the real world and deployed on a real robot. We conducted independent tests and obtained a 79.63% success rate with a 4mm gap. Simulation environments: https://github.com/0707yiliu/peg-in-hole-with-RL.