Learning Soft Robot Dynamics: An Uncertainty-Aware Active Exploration Framework

The authors propose an uncertainty-aware active exploration framework that can autonomously learn task-agnostic, generalizable dynamics models for soft robots. The method was evaluated on three simulation platforms—a continuum arm, an articulated fish in fluid, and a musculoskeletal leg—as well as on a real pneumatically actuated continuum arm. Compared with random exploration and task-specific baselines, it achieves higher modeling accuracy and yields higher returns in zero-shot control on unseen tasks, while remaining robust under sensing noise, actuation delays, and material nonlinearities.