arXiv cs.AI· Hehui Zheng, Bhavya Sukhija, Chenhao Li, Klemens Iten, Andreas Krause, Robert K. Katzschmann·· 11 小时前AI 评分31
用主动探索学习软体机器人动力学
Learning Soft Robotic Dynamics with Active Exploration
AI 导读
研究者提出一种不确定性感知的主动探索框架，可自主学习软体机器人系统与任务无关、可泛化的动力学模型。该框架在连续体机械臂、流体中的关节鱼和肌骨腿三个仿真平台及一台真实气动连续体机械臂上验证，相比随机探索和任务特定基线建模精度更高，并在未见任务的零样本控制中取得更高回报，且在传感噪声、驱动延迟和材料非线性下保持稳健。
正文 · 原文
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.
| Subjects: | Robotics (cs.RO); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2510.27428 [cs.RO] |
| (or arXiv:2510.27428v2 [cs.RO] for this version) | |
| https://doi.org/10.48550/arXiv.2510.27428 arXiv-issued DOI via DataCite |
Submission history
From: Hehui Zheng [view email]
[v1]
Fri, 31 Oct 2025 12:35:02 UTC (28,429 KB)
[v2]
Wed, 4 Nov 2026 15:27:23 UTC (1,747 KB)
来源：arXiv cs.AI · arxiv.org