PreCo: Enhancing Generalization in Co-Design of Modular Soft Robots via Brain-Body Pre-Training

Yuxing Wang, Shuang Wu, Tiantian Zhang, Yongzhe Chang, Haobo Fu, QIANG FU, Xueqian Wang

Keywords: Robot Co-design, Pre-training, Reinforcement Learning, Modular Soft Robots

TL;DR: Enhancing Generalization in Co-Design of Modular Soft Robots via Brain-Body Pre-Training

Abstract: Brain-body co-design, which involves the collaborative design of control strategies and morphologies, has emerged as a promising approach to enhance a robot's adaptability to its environment. However, the conventional co-design process often starts from scratch, lacking the utilization of prior knowledge. This can result in time-consuming and costly endeavors. In this paper, we present PreCo, a novel methodology that efficiently integrates brain-body pre-training into the co-design process of modular soft robots. PreCo is based on the insight of embedding co-design principles into models, achieved by pre-training a universal co-design policy on a diverse set of tasks. This pre-trained co-designer is utilized to generate initial designs and control policies, which are then fine-tuned for specific co-design tasks. Through experiments on a modular soft robot system, our method demonstrates zero-shot generalization to unseen co-design tasks, facilitating few-shot adaptation while significantly reducing the number of policy iterations required.

Student First Author: yes

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Website: https://yuxing-wang-thu.github.io/publication/2023-05-01-paper-title-number-1