Learning to Control Free-Form Soft Swimmers
Changyu Hu, Yanke Qu, Qiuan Yang, Xiaoyu Xiong, Kui Wu, Wei Li, Tao Du
Swimming in nature achieves remarkable performance through diverse morphological adaptations and intricate solid-fluid interaction, yet exploring this capability in artificial soft swimmers remains challenging due to high-dimensional control complexity and the computational cost of resolving hydrodynamic details. Traditional approaches rely on morphology-dependent heuristics—typically handcrafted by human experts through predefined muscle layouts, which constrain the control space to human-biased priors and limit exploration of novel swimming strategies. Additionally, robot learning frameworks often compromise hydrodynamic fidelity for computational efficiency, eliminating fluid dynamics essential for advanced strategies like vortex exploitation. Inspired by biological swimmers' ability to achieve complex motion through low-dimensional actuation, we propose an automated framework featuring a unified reduced mode control space that naturally captures typical deformation patterns observed across diverse swimmers. Our approach requires only sampling to generate controllers for diverse morphologies, minimizing human design effort while balancing kinematic expressiveness and dynamic plausibility. We also develop a GPU-accelerated simulator that efficiently resolves fluid-structure interactions, specifically tailored for learning swimming controllers. We evaluate our method in various morphologies, demonstrating 50\% higher success rates in swimming task than state-of-the-art baselines, which particularly struggle with unconventional forms (e.g. torus) where our method discovers novel, effective swimming strategies. We showcase how our automated approach discovers high-performance swimming patterns similar to those found in nature over years of evolution. Our work unlocks future opportunities in automated co-design of morphology and control of soft robots in complex hydrodynamic environments.