A Learning Approach to Robot-Agnostic Force-Guided High Precision Assembly

• URL: http://arxiv.org/abs/2010.08052v3
• Date: Mon, 2 Aug 2021 12:48:47 GMT
• Title: A Learning Approach to Robot-Agnostic Force-Guided High Precision Assembly
• Authors: Jieliang Luo and Hui Li
• Abstract summary: We propose a learning approach to high-precision robotic assembly problems. We focus on the contact-rich phase, where the assembly pieces are in close contact with each other. Our training environment is robotless, as the end-effector is not attached to any specific robot.
• Score: 6.062589413216726
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: In this work we propose a learning approach to high-precision robotic assembly problems. We focus on the contact-rich phase, where the assembly pieces are in close contact with each other. Unlike many learning-based approaches that heavily rely on vision or spatial tracking, our approach takes force/torque in task space as the only observation. Our training environment is robotless, as the end-effector is not attached to any specific robot. Trained policies can then be applied to different robotic arms without re-training. This approach can greatly reduce complexity to perform contact-rich robotic assembly in the real world, especially in unstructured settings such as in architectural construction. To achieve it, we have developed a new distributed RL agent, named Recurrent Distributed DDPG (RD2), which extends Ape-X DDPG with recurrency and makes two structural improvements on prioritized experience replay. Our results show that RD2 is able to solve two fundamental high-precision assembly tasks, lap-joint and peg-in-hole, and outperforms two state-of-the-art algorithms, Ape-X DDPG and PPO with LSTM. We have successfully evaluated our robot-agnostic policies on three robotic arms, Kuka KR60, Franka Panda, and UR10, in simulation. The video presenting our experiments is available at https://sites.google.com/view/rd2-rl

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