PP-Tac：灵巧机器人手中的触觉反馈纸张拾取
PP-Tac: Paper Picking Using Tactile Feedback in Dexterous Robotic Hands
AI总结：
针对机器人难以抓取薄软物体的问题，提出PP-Tac系统，利用多指触觉手实现滑动检测与力控制，结合扩散策略生成抓取运动，成功率达87.5%，为首次触觉灵巧手抓纸尝试。
摘要：
机器人日益被设想为人类伴侣，协助处理涉及操纵可变形物体的日常任务。尽管机器人硬件和具身人工智能的最新进展扩展了其能力，但当前系统在处理纸张和织物等薄、平且可变形物体时仍面临困难。这一局限性源于缺乏合适的感知技术，以在不同物体外观下进行稳健的状态估计，以及缺乏生成适当抓取运动的规划技术。为弥补这些差距，本文介绍了PP-Tac，一个用于拾取类纸物体的机器人系统。PP-Tac配备了一个多指机器人手，带有高分辨率全向触觉传感器\sensorname。这种硬件配置实现了实时滑动检测和在线摩擦力控制，从而减轻此类滑动。此外，抓取运动生成通过轨迹合成流程实现，该流程首先构建手指捏合运动的数据集。基于该数据集，训练了一个基于扩散的策略来控制手-臂机器人系统。实验表明，PP-Tac能够有效抓取不同材料、厚度和刚度的类纸物体，总体成功率达到87.5%。据我们所知，这项工作是首次使用触觉灵巧手抓取类纸可变形物体的尝试。我们的项目网页可在以下网址找到：https://peilin-666.github.io/projects/PP-Tac/
英文摘要：
Robots are increasingly envisioned as human companions, assisting with everyday tasks that often involve manipulating deformable objects. Although recent advances in robotic hardware and embodied AI have expanded their capabilities, current systems still struggle with handling thin, flat, and deformable objects such as paper and fabric. This limitation arises from the lack of suitable perception techniques for robust state estimation under diverse object appearances, as well as the absence of planning techniques for generating appropriate grasp motions. To bridge these gaps, this paper introduces PP-Tac, a robotic system for picking up paper-like objects. PP-Tac features a multi-fingered robotic hand with high-resolution omnidirectional tactile sensors \sensorname. This hardware configuration enables real-time slip detection and online frictional force control that mitigates such slips. Furthermore, grasp motion generation is achieved through a trajectory synthesis pipeline, which first constructs a dataset of finger's pinching motions. Based on this dataset, a diffusion-based policy is trained to control the hand-arm robotic system. Experiments demonstrate that PP-Tac can effectively grasp paper-like objects of varying material, thickness, and stiffness, achieving an overall success rate of 87.5\%. To our knowledge, this work is the first attempt to grasp paper-like deformable objects using a tactile dexterous hand. Our project webpage can be found at: https://peilin-666.github.io/projects/PP-Tac/
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
