Learning Soft Robotic Dynamics with Active Exploration

2025-10-31

AI Summary

Soft-bodied robots pose significant challenges for generalizable dynamics modeling due to their compliance, high-dimensional state-action spaces, and strong nonlinearities; existing data-driven approaches are limited by task-specific demonstrations or inefficient random exploration. This paper introduces SoftAE, a task-agnostic framework for dynamics learning: it employs a probabilistic ensemble network to quantify epistemic uncertainty and designs an uncertainty-driven active exploration strategy that autonomously identifies and samples underrepresented regions in the state-action space—enabling unsupervised, general-purpose dynamics modeling. Evaluated across diverse soft robotic platforms—including continuum arms, underwater fish-like robots, and pneumatic actuators—in both simulation and physical experiments, SoftAE consistently outperforms random exploration and task-specific baselines. It achieves high modeling accuracy, zero-shot control capability, and robustness against sensor noise, actuation delays, and material nonlinearities.

Technology Category

• Intelligent Robots: State Estimation
• Search and Optimization: Sampling/Simulation-based Search
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Soft robots offer unmatched adaptability and safety in unstructured environments, yet their compliant, high-dimensional, and nonlinear dynamics make modeling for control notoriously difficult. Existing data-driven approaches often fail to generalize, constrained by narrowly focused task demonstrations or inefficient random exploration. We introduce SoftAE, an uncertainty-aware active exploration framework that autonomously learns task-agnostic and generalizable dynamics models of soft robotic systems. SoftAE employs probabilistic ensemble models to estimate epistemic uncertainty and actively guides exploration toward underrepresented regions of the state-action space, achieving efficient coverage of diverse behaviors without task-specific supervision. We evaluate SoftAE on three simulated soft robotic platforms -- a continuum arm, an articulated fish in fluid, and a musculoskeletal leg with hybrid actuation -- and on a pneumatically actuated continuum soft arm in the real world. Compared with random exploration and task-specific model-based reinforcement learning, SoftAE produces more accurate dynamics models, enables superior zero-shot control on unseen tasks, and maintains robustness under sensing noise, actuation delays, and nonlinear material effects. These results demonstrate that uncertainty-driven active exploration can yield scalable, reusable dynamics models across diverse soft robotic morphologies, representing a step toward more autonomous, adaptable, and data-efficient control in compliant robots.

Problem

• Learning soft robot dynamics with active exploration
• Improving generalization of data-driven control models
• Addressing nonlinear compliant system modeling challenges

Innovation

• Uncertainty-aware active exploration for soft robots
• Probabilistic ensemble models guide state-action coverage
• Task-agnostic dynamics learning enables zero-shot control