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

翻译：操作可变形物体一直是机器人领域的挑战，其难点在于状态估计、长时域规划以及预测交互后物体的形变——尤以三维可变形物体为甚。我们提出SculptDiff——一种基于目标条件的扩散模仿学习框架，该框架利用点云状态观测，直接学习针对多种目标形状的黏土雕塑策略。据我们所知，这是首个成功学习三维可变形物体操作策略的真实世界方法。雕塑视频、数据集及硬件CAD模型详见项目网站：https://sites.google.com/andrew.cmu.edu/imitation-sculpting/home