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This research presents a data-efficient framework for modeling soft robotic dynamics using uncertainty-aware active exploration, addressing the generalization challenges inherent in compliant systems. By autonomously collecting informative data rather than relying on random sampling or narrow task demonstrations, the method achieves higher accuracy and robust zero-shot control across diverse simulated and real-world platforms. The approach significantly advances autonomous control capabilities for soft robots by handling material nonlinearities and sensing noise effectively.
Learning Soft Robotic Dynamics with Active Exploration
arXiv:2510.27428v2 Announce Type: replace-cross Abstract: Soft robots offer adaptability and safety in unstructured environments, yet their compliant, high-dimensional, and nonlinear dynamics make modeling for control difficult. Existing data-driven approaches often fail to generalize, constrained by narrowly focused task demonstrations or inefficient random exploration. We introduce an uncertainty-aware active exploration framework that autonomously learns task-agnostic and generalizable dynamics models of soft robotic systems. We evaluate this framework on three simulated platforms---a continuum arm, an articulated fish in fluid, and a musculoskeletal leg---and on a real-world pneumatically actuated continuum arm. Compared with random exploration and task-specific baseline, our method achieves higher modeling accuracy by actively collecting informative data. This improved model quality yields higher returns in zero-shot control on unseen tasks and robust performance under sensing noise, actuation delays, and material nonlinearities. These results demonstrate that uncertainty-driven active exploration enables data-efficient dynamics learning across soft robotic morphologies, supporting autonomous and adaptable control for compliant robots.
Abstract:Soft robots offer adaptability and safety in unstructured environments, yet their compliant, high-dimensional, and nonlinear dynamics make modeling for control difficult. Existing data-driven approaches often fail to generalize, constrained by narrowly focused task demonstrations or inefficient random exploration. We introduce an uncertainty-aware active exploration framework that autonomously learns task-agnostic and generalizable dynamics models of soft robotic systems. We evaluate this framework on three simulated platforms---a continuum arm, an articulated fish in fluid, and a musculoskeletal leg---and on a real-world pneumatically actuated continuum arm. Compared with random exploration and task-specific baseline, our method achieves higher modeling accuracy by actively collecting informative data. This improved model quality yields higher returns in zero-shot control on unseen tasks and robust performance under sensing noise, actuation delays, and material nonlinearities. These results demonstrate that uncertainty-driven active exploration enables data-efficient dynamics learning across soft robotic morphologies, supporting autonomous and adaptable control for compliant robots.
Robotics (cs.RO); Artificial Intelligence (cs.AI)
arXiv:2510.27428 [cs.RO]
(or arXiv:2510.27428v2 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2510.27428 Focus to learn more arXiv-issued DOI via DataCite
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