发表机构
Sapienza University of Rome; University of Twente（罗马大学; 特文特大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出仅用软光学触觉传感在线估计接触力与任务坐标系的方法，通过自标记学习映射触觉图像至接触变量，并经扩展卡尔曼滤波融合，实现仅靠触觉的闭环力控制。
AI 中文摘要
混合力/运动控制需要了解交互力以及定义力控和运动控方向的任务坐标系。这些量通常通过力/力矩传感或基于模型的残差获得，且常常假设一个标称环境模型。本研究在局部平面接触且接触力矩可忽略的假设下，仅利用软光学触觉传感，解决接触力以及可能随时间变化的任务坐标系的在线估计问题。所提方法将软光学触觉传感器变形弹性体的单幅图像映射到可观测的接触变量：压入深度、两个表面到传感器的倾斜角以及三维接触力，每个变量均带有逐样本的不确定性估计。该映射通过自标记采集程序学习，其中机械臂施加受控接触，同时辅助力/力矩传感器离线提供真值标签。随后，触觉测量与机器人本体感觉数据在扩展卡尔曼滤波器中融合，生成接触任务坐标系和交互力的连续更新估计。使用安装在UR10机械臂上的DigiTac传感器进行的控制实验表明，在操作员进行的线性和角运动中，仅以触觉作为唯一外部感觉反馈，即可实现对平面刚性板的闭环接触力调节。
英文摘要
Hybrid force/motion control requires knowledge of the interaction force and of a task frame defining the force- and motion-controlled directions. These quantities are usually obtained from force/torque sensing or model-based residuals, often assuming also a nominal environment model. This work addresses the online estimation of the contact force and a possibly time-varying task frame using only soft optical tactile sensing, under the assumption of locally planar contact with a negligible contact moment. The proposed method maps a single image of the deformed elastomer of a soft optical tactile sensor to observable contact variables: indentation depth, two surface-to-sensor tilt angles, and 3D contact force, each with a per-sample uncertainty estimate. The mapping is learned through a self-labeling acquisition procedure, in which a manipulator imposes controlled contacts while an auxiliary Force/Torque sensor is used offline to provide ground-truth labels. The tactile measurement is then fused with robot proprioceptive data in an Extended Kalman Filter, producing a continuously updated estimate of the contact task frame and of the interaction force. Control experiments with a DigiTac sensor mounted on a UR10 manipulator demonstrate closed-loop contact force regulation against a flat rigid board in linear and angular motion by a human operator, with touch as the only exteroceptive feedback.
Comments14 pages, 9 figures, 7 tables. Video: https://youtube.com/playlist?list=PLejKMZmW8AvI. Dataset: https://doi.org/10.5281/zenodo.23209032
