Planning robotic manipulation tasks, especially those that involve interaction between deformable and rigid objects, is challenging due to the complexity in predicting such interactions. We introduce SPONGE, a sequence planning pipeline powered by a deep learning-based contact prediction model for contacts between deformable and rigid bodies under interactions. The contact prediction model is trained on synthetic data generated by a developed simulation environment to learn the mapping from point-cloud observation of a rigid target object and the pose of a deformable tool, to 3D representation of the contact points between the two bodies. We experimentally evaluated the proposed approach for a dish cleaning task both in simulation and on a real \panda with real-world objects. The experimental results demonstrate that in both scenarios the proposed planning pipeline is capable of generating high-quality trajectories that can accomplish the task by achieving more than 90\% area coverage on different objects of varying sizes and curvatures while minimizing travel distance. Code and video are available at: \url{https://irobotics.aalto.fi/sponge/}.

翻译：规划涉及可变形与刚性物体交互的机器人操作任务，因预测此类交互的复杂性而极具挑战。本文提出SPONGE——一种由基于深度学习的接触预测模型驱动的序列规划流程，专门用于交互场景下可变形体与刚体之间的接触预测。该接触预测模型利用开发仿真环境生成的合成数据训练，学习从刚性目标物体的点云观测和可变形工具的位姿到两物体接触点三维表示的映射。我们针对餐具清洁任务进行了实验评估，涵盖仿真环境与配备真实物体的实体Panda机器人平台。实验结果表明，在两种场景下，所提出的规划流程均能生成高质量轨迹，在覆盖面积超过90%且最小化移动距离的条件下，完成不同尺寸与曲率物体的清洁任务。代码与视频详见：\url{https://irobotics.aalto.fi/sponge/}。