Co-Design and Morphology-Guided Feedback Control: An Approach for Soft Robots

Abstract

Soft robots, with their highly compliant bodies, exhibit numerous unforeseen configurations that often defy engineering intuition and complicate control design. This work introduces a simulation-based co-optimization framework that jointly optimizes both morphology and control. Unlike existing approaches that rely on oversimplified soft robot models or feed-forward controllers for simple tasks, our framework targets complex tasks that benefit from closed-loop feedback. The controller is trained over a hybrid design space combining discrete parameters, which define the nominal structure, and continuous parameters, which shift the morphology adaptively. The design distribution is iteratively manipulated to emphasize high-performing candidates until the optimal design–control pair emerges. Proprioceptive feedback in the form of mechanical strain is integrated to provide the controller with awareness of morphological state and interaction dynamics. Demonstrations show that the framework converges reliably to optimal design–control solutions, validating the effectiveness of the proposed joint optimization strategy.