SculptDiff: Learning Robotic Clay Sculpting from Humans with Goal Conditioned Diffusion Policy
<|reference_start|>SculptDiff: Learning Robotic Clay Sculpting from Humans with Goal Conditioned Diffusion Policy: 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<|reference_end|>
@article{bartsch2024sculptdiff:,
    title={SculptDiff: Learning Robotic Clay Sculpting from Humans with Goal
  Conditioned Diffusion Policy},
    author={Alison Bartsch, Arvind Car, Charlotte Avra, Amir Barati Farimani},
    journal={arXiv preprint arXiv:2403.10401},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.10401},
    primaryClass={cs.RO cs.AI}
}