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:在机器人操作任务中,特别是涉及到变形体和刚性物体之间交互的任务中,由于预测这种交互的复杂性而具有挑战性。我们引入了SPONGE,一种由深度学习驱动的接触预测模型支持的序列规划流程,用于变形和刚性物体之间的接触。 接触预测模型是在开发的仿真环境中生成的合成数据上训练的,以学习从刚性目标对象的点云观测和变形工具的姿态到两个物体之间接触点的三维表示的映射。我们在模拟和真实的 \panda 机器人上分别对提出的方法进行了实验评估。实验结果表明,在两种情况下,所提出的规划流程都能够生成能够完成任务的高质量轨迹,并可在不同尺寸和曲率的不同对象上实现超过90%的区域覆盖率,同时尽量减少行驶距离。代码和视频可在以下网址中获得:\url{https://irobotics.aalto.fi/sponge/}。