Imitation Learning-Based Path Generation for the Complex Assembly of Deformable Objects

TL;DR

This paper presents a learning-based approach to generate and refine paths for deformable object assembly by combining simple models, human demonstrations, and behavior cloning to improve motion planning.

Contribution

It introduces a method that uses human demonstrations and behavior cloning to enable effective path planning for deformable objects with simple models, reducing reliance on complex dynamics.

Findings

Successful generation of collision-free paths using simple models

Effective human-in-the-loop path modification

Behavior cloning produces dexterous policies for task completion

Abstract

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.