Learning Soft Robotic Dynamics with Active Exploration
Overview
This research introduces an uncertainty-aware active exploration framework to learn task-agnostic and generalizable dynamics models for soft robotic systems. The method is evaluated on three simulated platforms—a continuum arm, an articulated fish in fluid, and a musculoskeletal leg—as well as a real-world pneumatically actuated continuum arm. Compared with random exploration and task-specific baselines, the approach achieves higher modeling accuracy by actively collecting informative data. Improved model quality leads to better zero-shot control performance on unseen tasks and robust operation under sensing noise, actuation delays, and material nonlinearities. The results demonstrate that uncertainty-driven active exploration enables data-efficient dynamics learning across diverse soft robotic morphologies, supporting autonomous and adaptable control for compliant robots.
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“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 co”
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- arXiv Robotics — research abstractsSignalLearning Soft Robotic Dynamics with Active Exploration
This research introduces an uncertainty-aware active exploration framework to learn task-agnostic and generalizable dynamics models for soft robotic systems. The method is evaluated on three simulated platforms—a continuum arm, an articulated fish in fluid, and a musculoskeletal leg—as well as a real-world pneumatically actuated continuum arm. Compared with random exploration and task-specific baselines, the approach achieves higher modeling accuracy by actively collecting informative data. Improved model quality leads to better zero-shot control performance on unseen tasks and robust operation under sensing noise, actuation delays, and material nonlinearities. The results demonstrate that uncertainty-driven active exploration enables data-efficient dynamics learning across diverse soft robotic morphologies, supporting autonomous and adaptable control for compliant robots.
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