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.

翻译：软体机器人在非结构化环境中展现出无与伦比的适应性与安全性，但其柔顺、高维且非线性的动力学特性使得面向控制的建模极为困难。现有数据驱动方法常因局限于狭窄的任务演示或低效的随机探索而难以泛化。本文提出SoftAE，一种不确定性感知的主动探索框架，能够自主学习与任务无关且可泛化的软体机器人系统动力学模型。SoftAE采用概率集成模型估计认知不确定性，并主动引导探索朝向状态-动作空间中未被充分覆盖的区域，从而在没有任务特定监督的情况下高效覆盖多样化的行为模式。我们在三个仿真软体机器人平台（连续体机械臂、流体中铰接鱼形机器人、混合驱动的肌肉骨骼腿）以及一个真实的气动连续体软体机械臂上评估SoftAE。与随机探索及任务特定的基于模型的强化学习方法相比，SoftAE生成的动力学模型更精确，在未见任务上实现了更优异的零样本控制性能，并在传感噪声、驱动延迟和非线性材料效应下保持了鲁棒性。这些结果表明，不确定性驱动的主动探索能够为不同形态的软体机器人构建可扩展、可复用的动力学模型，为柔顺机器人实现更自主、适应性强且数据高效的控制迈出了重要一步。