Soft-bodied organisms such as octopuses and elephant trunks exhibit remarkable morphological adaptability, dynamically reconfiguring body shape and stiffness, and flexibly adjusting their control strategies to enable versatile behaviors. Inspired by these biological systems, various soft robots have emerged in recent decades, featuring diverse materials, stiffnesses, and morphologies tailored to specific tasks. Despite substantial advances in the materials and structural designs of soft robots, developing a generalizable control framework capable of rapid adaptation across diverse configurations remains a long-standing challenge. Existing controllers are limited to fixed configurations, demanding laborious configuration-specific remodelling and policy redesign for new configurations. Here, we introduce a generalizable control system that enables rapid adaptation across diverse soft robot configurations via reinforcement learning in a shared linear Koopman embedding space. By encoding robot dynamics into this embedding space, our method decouples control policies from specific morphologies, allowing real-time, model-free policy adaptation across diverse configurations without retraining from scratch. We validate our system across 33 distinct robot configurations. Our system achieves a 75 times reduction in transfer samples across configurations, while sustaining robust performance under high-speed motion, heavy payloads, and multiactuator faults, and achieving real-world skills previously unattainable in soft robotics. This work establishes a unified and adaptable control paradigm for diverse soft robot configurations, bridging mechanical reconfigurability with control flexibility, and may offer broader insights for generalizable control in complex physical systems.

翻译：软体生物如章鱼和象鼻展现出卓越的形态适应性，能够动态重构身体形状与刚度，并灵活调整控制策略以实现多样化的行为。受这些生物系统启发，近几十年来涌现出多种软体机器人，其采用针对特定任务定制的不同材料、刚度和形态。尽管软体机器人的材料与结构设计取得了重大进展，但开发一个能够跨多种构型快速适应的泛化控制框架仍是长期存在的挑战。现有控制器局限于固定构型，需要针对新构型进行耗时的特定构型重构和策略再设计。本文提出一种泛化控制系统，通过在线性Koopman嵌入空间中进行强化学习，实现跨多种软体机器人构型的快速适应。通过将机器人动力学编码至该嵌入空间，我们的方法将控制策略与具体形态解耦，使得无需从头重新训练即可在多样构型间实现实时无模型策略适应。我们在33种不同机器人构型上验证了该系统，其跨构型的迁移样本量减少了75倍，同时在高速度运动、重载荷及多执行器故障条件下保持稳健性能，并实现了软体机器人领域此前难以企及的真实世界技能。本工作为多样软体机器人构型建立了统一且可适应的控制范式，将机械可重构性与控制灵活性相衔接，或可为复杂物理系统中的泛化控制提供更广泛的洞见。