提出基于轨迹对齐的机器人动作学习方法，解决传统方法在扰动下的稳定性问题。
An Alignment-Based Approach to Learning Motions from Demonstrations
- 以平均轨迹为基准进行对齐，避免时间或状态依赖的局限
- 在2D数据集上实现多模态行为生成并保持扰动鲁棒性
- 适用于7自由度机械臂在三个任务领域的动作学习
示范学习（LfD）已证明能为机器人提供多种场景下的基础运动技能。现有研究可分为依赖时间或不依赖时间的系统，前者无法处理重叠轨迹，后者在扰动下易产生异常行为。本文提出基于轨迹对齐的示范学习框架（CALM），通过与示范动作的平均轨迹对齐，而非单纯依赖时间或状态。我们分析了CALM的收敛性，提出可应对扰动导致偏移的对齐技术，并利用示范聚类生成多模态行为。实验表明，CALM在2D数据集上有效缓解了时序与非时序方法的缺陷，并在7自由度机器人上成功应用于三个领域任务。
原文摘要 · Abstract (English)
Learning from Demonstration (LfD) has shown to provide robots with fundamental motion skills for a variety of domains. Various branches of LfD research (e.g., learned dynamical systems and movement primitives) can generally be classified into ''time-dependent'' or ''time-independent'' systems. Each provides fundamental benefits and drawbacks -- time-independent methods cannot learn overlapping trajectories, while time-dependence can result in undesirable behavior under perturbation. This paper introduces Cluster Alignment for Learned Motions (CALM), an LfD framework dependent upon an alignment with a representative ''mean" trajectory of demonstrated motions rather than pure time- or state-dependence. We discuss the convergence properties of CALM, introduce an alignment technique able to handle the shifts in alignment possible under perturbation, and utilize demonstration clustering to generate multi-modal behavior. We show how CALM mitigates the drawbacks of time-dependent and time-independent techniques on 2D datasets and implement our system on a 7-DoF robot learning tasks in three domains.
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