Robotic in-hand manipulation requires reliable object-motion tracking under frequent visual occlusion, yet low-texture visuotactile images provide few stable correspondences for conventional image- or geometry-matching methods. This paper presents TacSE3, a tactile motion-estimation pipeline that converts low-texture visuotactile observations into a decoupled three-dimensional force field and estimates incremental rigid-body motion on SE(3). The method derives planar translation from contact-centroid motion and estimates rotation primarily from shear-related tactile responses, yielding a physically interpretable signal for in-gripper tracking and compensation. Experiments with paired DM-Tac fingertip sensors show that dual-sensor sensing reduces translation-rotation ambiguity, supports rotation tracking across axes and object geometries, and provides a lightweight compensation signal that improves disturbance tolerance in downstream manipulation tasks without retraining the base policy.

翻译：机器人手内操作需要在频繁视觉遮挡下实现可靠的目标运动跟踪，然而低纹理的视触觉图像为传统图像或几何匹配方法提供的稳定对应点十分有限。本文提出TacSE3，一种将低纹理视触觉观测转换为解耦三维力场并估计SE(3)上增量刚体运动的触觉运动估计流程。该方法通过接触质心运动推导平面平移，并主要利用剪切相关触觉响应估计旋转，从而为手内跟踪与补偿提供物理可解释的信号。采用配对DM-Tac指尖传感器的实验表明，双传感器感知能够减少平移-旋转歧义，支持跨轴与跨物体几何形状的旋转跟踪，并提供轻量级补偿信号，可在不重新训练基础策略的情况下提升下游操作任务中的扰动容忍能力。