SculptDiff: Learning Robotic Clay Sculpting from Humans with Goal Conditioned Diffusion Policy
Alison Bartsch, Arvind Car, Charlotte Avra, Amir Barati Farimani
Abstract
Manipulating deformable objects remains a challenge within robotics due to the difficulties of state estimation, long-horizon planning, and predicting how the object will deform given an interaction. These challenges are the most pronounced with 3D deformable objects. We propose SculptDiff, a goal-conditioned diffusion-based imitation learning framework that works with point cloud state observations to directly learn clay sculpting policies for a variety of target shapes. To the best of our knowledge this is the first real-world method that successfully learns manipulation policies for 3D deformable objects. For sculpting videos and access to our dataset and hardware CAD models, see the project website: https://sites.google.com/andrew.cmu.edu/imitation-sculpting/home
BibTeX
@inproceedings{iros2024_sculptdifflearni,
title = {SculptDiff: Learning Robotic Clay Sculpting from Humans with Goal Conditioned Diffusion Policy},
author = {Alison Bartsch and Arvind Car and Charlotte Avra and Amir Barati Farimani},
booktitle = {IROS 2024},
year = {2024}
}