Computational co-design of structure and feedback controller for locomoting soft robots

TL;DR

This paper introduces a novel co-design approach for soft robots that simultaneously optimizes structure and feedback control using topology optimization and neural networks, improving locomotion adaptability on uncertain terrains.

Contribution

It presents an integrated optimization framework combining topology optimization and neural network-based control for soft robots, addressing the challenge of joint design under terrain uncertainty.

Findings

Effective joint optimization of structure and control demonstrated

Improved locomotion performance on uncertain terrains shown

Method outperforms traditional stochastic search approaches

Abstract

Soft robots have gained significant attention due to their flexibility and safety, particularly in human-centric applications. The co-design of structure and controller in soft robotics has presented a longstanding challenge owing to the complexity of the dynamics involved. Despite some pioneering work dealing with the co-design of soft robot structures and actuation, design freedom has been limited by stochastic design search approaches. This study proposes the simultaneous optimization of structure and controller for soft robots in locomotion tasks, integrating topology optimization-based structural design with neural network-based feedback controller design. Here, the feedback controller receives information about the surrounding terrain and outputs actuation signals that induce the expansion and contraction of the material. We formulate the simultaneous optimization problem under uncertainty in terrains and construct an optimization algorithm that utilizes automatic differentiation within topology optimization and neural networks. We present numerical experiments to demonstrate the validity and effectiveness of our proposed method.