用人类示范学习生成可变形物体装配路径，降低对复杂动力学模型依赖。
Imitation Learning-Based Path Generation for the Complex Assembly of Deformable Objects
- 基于简单模型生成大量参考路径，结合人机协作修正。
- 通过行为克隆训练出能精准跟随路径的智能策略。
- 适合需高精度装配但难以建模的柔性物体场景。
本文研究如何利用学习方法简化可变形物体高质路径的设计。操纵可变形物体时，物体动力学起关键作用，因此运动规划常依赖详细模型。我们提出仅使用可变形物体的简单动力学模型，结合人类示范与学习来实现运动规划。具体而言，先基于简单模型进行离线无碰撞路径规划，生成大量参考路径；随后在机器人上执行这些路径，采用柔顺控制使人类可微调路径以成功完成任务；最后，基于虚拟路径数据集和人类修正后的路径，使用行为克隆（BC）训练出能精准跟随参考路径完成任务的灵巧策略。
原文摘要 · Abstract (English)
This paper investigates how learning can be used to ease the design of high-quality paths for the assembly of deformable objects. Object dynamics plays an important role when manipulating deformable objects; thus, detailed models are often used when conducting motion planning for deformable objects. We propose to use human demonstrations and learning to enable motion planning of deformable objects with only simple dynamical models of the objects. In particular, we use the offline collision-free path planning, to generate a large number of reference paths based on a simple model of the deformable object. Subsequently, we execute the collision-free paths on a robot with a compliant control such that a human can slightly modify the path to complete the task successfully. Finally, based on the virtual path data sets and the human corrected ones, we use behavior cloning (BC) to create a dexterous policy that follows one reference path to finish a given task.
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