解决机器人运动规划中的执行偏差问题，提升轨迹跟踪稳定性。
A Robust Task-Level Control Architecture for Learned Dynamical Systems
- 在动态系统基础上加入稳定控制器与自适应控制器
- 使用窗口化DTW选择目标，应对时间错位问题
- 在手写数据集上验证，显著改善相位一致性
基于动力学系统（DS）的示教学习（LfD）是生成机器人任务空间运动规划的强大工具。然而，未建模动态、持续扰动和系统延迟常导致机器人任务空间状态偏离期望值，造成‘任务-执行不匹配’。本文提出一种新型鲁棒任务级控制架构——L1-增强动力学系统（L1-DS），可显式处理任意基于DS的LfD方案生成的轨迹中出现的任务-执行不匹配问题。该框架为任一基于DS的LfD模型添加了名义稳定控制器与L1自适应控制器。此外，引入基于窗口化动态时间规整（DTW）的目标选择器，使名义稳定控制器能有效应对时间错位，提升相位一致性的跟踪性能。我们在LASA和IROS手写数据集上验证了该架构的有效性。
原文摘要 · Abstract (English)
Dynamical system (DS)-based learning from demonstration (LfD) is a powerful tool for generating motion plans in the operation ('task') space of robotic systems. However, realizing generated motion plans is often compromised by a "task-execution mismatch", where unmodeled dynamics, persistent disturbances, and system latency cause the robot's task-space state to diverge from the desired state. We propose a novel task-level robust control architecture, L1-augmented Dynamical Systems (L1-DS), that explicitly handles the task-execution mismatch in tracking a nominal motion plan generated by any DS-based LfD scheme. Our framework augments any DS-based LfD model with a nominal stabilizing controller and an L1 adaptive controller. Furthermore, we introduce a windowed Dynamic Time Warping (DTW)-based target selector, which enables the nominal stabilizing controller to handle temporal misalignment for improved phase-consistent tracking. We demonstrate the efficacy of our architecture on the LASA and IROS handwriting datasets.
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