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

arXiv: https://arxiv.org/abs/2505.24339 · HTML: https://mlsanity.com/paper/2505.24339/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Robot manipulation imitation learning, one of 13 directions in Offline RL and imitation learning from data.
- Builds on 3 of its 24 references that are in the map.
- First posted on arXiv 30 May 2025.

## Placed in

- Offline RL and imitation learning from data / [Robot manipulation imitation learning](https://mlsanity.com/offlinerl/robot-manipulation-imitation-learning/)

## Builds on (most cited first, 3 of 3)

- [An Algorithmic Perspective on Imitation Learning](https://mlsanity.com/paper/1811.06711/) (2018)
- [Combining Self-Supervised Learning and Imitation for Vision-Based Rope Manipulation](https://mlsanity.com/paper/1703.02018/) (2017)
- [Folding Deformable Objects using Predictive Simulation and Trajectory Optimization](https://mlsanity.com/paper/1512.06922/) (2015)

## Abstract (arXiv)

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.