Applications in fields ranging from home care to warehouse fulfillment to surgical assistance require robots to reliably manipulate the shape of 3D deformable objects. Analytic models of elastic, 3D deformable objects require numerous parameters to describe the potentially infinite degrees of freedom present in determining the object's shape. Previous attempts at performing 3D shape control rely on hand-crafted features to represent the object shape and require training of object-specific control models. We overcome these issues through the use of our novel DeformerNet neural network architecture, which operates on a partial-view point cloud of the manipulated object and a point cloud of the goal shape to learn a low-dimensional representation of the object shape. This shape embedding enables the robot to learn a visual servo controller that computes the desired robot end-effector action to iteratively deform the object toward the target shape. We demonstrate both in simulation and on a physical robot that DeformerNet reliably generalizes to object shapes and material stiffness not seen during training. Crucially, using DeformerNet, the robot successfully accomplishes three surgical sub-tasks: retraction (moving tissue aside to access a site underneath it), tissue wrapping (a sub-task in procedures like aortic stent placements), and connecting two tubular pieces of tissue (a sub-task in anastomosis).

翻译：从家庭护理到仓库管理再到手术辅助等领域的应用要求机器人能够可靠地操控三维可变形物体的形状。对弹性三维可变形物体的分析模型需要大量参数来描述决定物体形状时可能存在的无限自由度。以往尝试进行三维形状控制的方法依赖于手工设计的特征来表示物体形状，并需针对特定物体训练控制模型。我们通过提出新颖的DeformerNet神经网络架构克服了这些问题，该架构基于被操控物体的部分视角点云和目标形状点云，学习物体形状的低维表示。这种形状嵌入使机器人能够学习视觉伺服控制器，计算所需的机器人末端执行器动作，逐步将物体变形至目标形状。我们在仿真环境和实体机器人上证明，DeformerNet能够可靠地泛化至训练中未见的物体形状和材料刚度。关键在于，利用DeformerNet，机器人成功完成了三项手术子任务：组织牵开（将组织移开以暴露下方手术部位）、组织包裹（如主动脉支架置入等手术中的子任务），以及连接两段管状组织（吻合术中的子任务）。