2024/03/15 by Alison Bartsch, Arvind Car, Bartsch, Alison +5 · 2 citations
Engineering · #Artificial Intelligence (cs.AI) #Evacuation and Crowd Dynamics #FOS: Computer and information sciences #Innovations in Concrete and Construction Materials #Robotics (cs.RO)
paper · pdf · doi:10.48550/arxiv.2403.10401
openalex publication_date 2024/03/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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