用时空管路学习未知系统的演示动作，实现高效安全控制。
Learning from Demonstration via Spatiotemporal Tubes for Unknown Euler-Lagrange Systems
- 将示范数据建模为时变精度约束的时空管路
- 无需系统辨识即可在限幅下稳定跟踪演示轨迹
- 适合机器人运动控制，尤其对动力学未知场景
我们提出STT-LfD，一种统一的从示范学习（LfD）框架，用于未知欧拉-拉格朗日系统。与传统解耦方法不同，该方法将示范视为数据驱动的安全规范。利用异方差高斯过程，STT-LfD学习时空管路（STTs），作为捕捉任务时变精度需求的意图包络。闭式反馈控制器在遵守执行器限制的前提下，强制执行这些学习到的约束，而无需显式系统辨识。该方法保持了示范的时间结构，计算效率高，并避免了显式系统识别。在移动机器人和7自由度机械臂上的硬件实验表明，其在抗干扰鲁棒性和计算速度方面优于基线方法。
原文摘要 · Abstract (English)
We present STT-LfD, a unified Learning from Demonstration (LfD) framework that integrates motion learning with control for unknown Euler-Lagrange systems. Unlike traditional decoupled approaches that track a fixed reference, the proposed method treats demonstrations as a data-driven safety specification. Using heteroscedastic Gaussian Processes, STT-LfD learns Spatiotemporal Tubes (STTs) as an intent envelope that capture time-varying precision requirements of a task. A closed-form feedback controller then enforces these learned constraints while respecting actuator limits, without requiring explicit system identification. The approach preserves the temporal structure of demonstrations, remains computationally efficient, and avoids explicit system identification. Hardware experiments on a mobile robot and a 7-DOF manipulator show that it outperforms baselines in robustness to disturbances and computational speed.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。