Dense collections of movable objects are common in everyday spaces-from cabinets in a home to shelves in a warehouse. Safely retracting objects from such collections is difficult for robots, yet people do it frequently, leveraging learned experience in tandem with vision and non-prehensile tactile sensing on the sides and backs of their hands and arms. We investigate the role of contact force sensing for training robots to gently reach into constrained clutter and extract objects. The available sensing modalities are (1) "eye-in-hand" vision, (2) proprioception, (3) non-prehensile triaxial tactile sensing, (4) contact wrenches estimated from joint torques, and (5) a measure of object acquisition obtained by monitoring the vacuum line of a suction cup. We use imitation learning to train policies from a set of demonstrations on randomly generated scenes, then conduct an ablation study of wrench and tactile information. We evaluate each policy's performance across 40 unseen environment configurations. Policies employing any force sensing show fewer excessive force failures, an increased overall success rate, and faster completion times. The best performance is achieved using both tactile and wrench information, producing an 80% improvement above the baseline without force information.

翻译：日常生活中，可移动物体的密集堆积场景十分常见——从家庭橱柜到仓库货架。机器人从这类堆积中安全抽取物体具有挑战性，而人类却能频繁完成该任务，这得益于其结合视觉感知与手背、手臂非抓握触觉传感的习得经验。本研究探讨接触力传感在训练机器人轻柔伸入受限杂乱环境并抽取物体中的作用。可用的传感模态包括：（1）“眼在手”视觉系统，（2）本体感知，（3）非抓握三轴触觉传感，（4）基于关节力矩估计的接触力矩，（5）通过监测吸盘真空管路获取的物体吸附状态度量。我们采用模仿学习方法，基于随机生成场景的演示数据集训练策略，并对力矩与触觉信息进行消融实验。在40种未见环境配置中评估各策略性能。采用任何力传感的策略均表现出更少的过力失效案例、更高的整体成功率及更短的任务完成时间。结合触觉与力矩信息的策略取得最佳性能，相比无力信息基线实现了80%的性能提升。