Reinforcement learning in linear embedding space unlocks generalizable control across soft robot configurations

Soft-bodied organisms exhibit prominent morphological adaptability, dynamically reconfiguring shape and stiffness to achieve versatile behaviors. Inspired by these systems, soft robots with diverse morphologies have emerged, yet a unified control framework that rapidly adapts across configurations remains elusive. Here, we introduce a generalizable control system that enables rapid cross-configuration adaptation 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 without retraining from scratch. We validate our system across 33 distinct robot configurations. Our system achieves a 75 × 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 an adaptable control framework for diverse soft robot configurations and may offer insights for generalizable control in complex physical systems.