用触觉与力矩感知让机器人在杂乱中轻柔取物
Gentle Object Retraction in Dense Clutter Using Multimodal Force Sensing and Imitation Learning
- 结合视觉、本体感知和非抓握式触觉/力矩传感
- 融合触觉与力矩信息使成功率提升80%
- 适合需要精细操作的仓储或家庭服务场景
日常空间中密集摆放的可移动物体（如家用橱柜或仓库货架）给机器人安全取物带来挑战。人类凭借经验，结合视觉与手背、手臂的非抓握式触觉感知完成该任务。本文研究接触力感知在训练机器人轻柔深入拥挤环境提取物体中的作用。采用五种感知模态：(1) 手眼视觉，(2) 本体感知，(3) 非抓握三轴触觉，(4) 从关节扭矩估算的接触力矩，(5) 通过吸盘真空管监测物体获取状态。使用模仿学习在随机生成场景上训练策略，并进行力矩与触觉信息的消融实验。在40个未见环境配置中评估性能。任何引入力感知的策略均减少过度用力失败，提升整体成功率并缩短完成时间；同时使用触觉与力矩信息的策略表现最佳，相比无力感知基线提升80%。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。