GLoTouch：使用平行夹爪的全局到局部触觉感知，用于无外部视觉的物体搜索、识别和抓取

中文摘要 AI 辅助

感知环境中的物体是自主机器人的一项基本能力。在黑暗或低光照环境中，外部摄像头往往无法可靠地感知物体的位置和几何形状；当视觉感知不可用时，仅通过触觉完成目标搜索、识别和抓取成为一项关键的机器人操作能力。该任务必须同时处理容器尺度的空间探索和物体尺度的细粒度几何感知，这对于低自由度的平行夹爪来说尤其具有挑战性。然而，目前仍缺乏一个统一的框架来连接容器尺度的空间探索与物体尺度的细粒度几何感知及抓取。为了应对这一挑战，我们提出了GLoTouch，一个基于平行夹爪的全局到局部触觉感知与操作框架。在全局阶段，夹爪持有一根被动的长距离探针，结合力测量与已知的工具几何形状来定位接触点，并主动估计候选物体的位置、粗略轮廓和高度。在局部阶段，机器人放下探针，利用同一夹爪上的双侧视觉触觉传感器直接获取局部触觉观测，这些观测与给定的目标3D模型进行匹配，无需针对特定物体进行训练。我们在仿真和真实机器人实验中评估了该框架。源代码将开源。

英文摘要

Perceiving objects in the environment is a fundamental capability of autonomous robots. In dark or low-light environments, external cameras often fail to reliably perceive object positions and geometry; when visual sensing is unavailable, completing target search, recognition, and grasping through touch alone becomes a key robot manipulation capability. This task must simultaneously address container-scale spatial exploration and object-scale fine-grained geometric perception, which is particularly challenging for low-degree-of-freedom parallel grippers. However, a unified framework remains lacking for connecting container-scale spatial exploration with object-scale fine-grained geometric perception and grasping. To address this challenge, we present \textbf{GLoTouch}, a global-to-local haptic perception and manipulation framework built on a parallel gripper. In the global stage, the gripper holds a passive long-reach probe, combining force measurements with known tool geometry to localize contacts and actively estimate candidate-object positions, coarse contours, and heights. In the local stage, the robot sets down the probe and uses the bilateral visuotactile sensors on the same gripper to directly acquire local haptic observations, which are matched against a given target 3-D model without object-specific training. We evaluate the framework in both simulation and real-robot experiments. Source code will be open-sourced.

发表机构

• Shanghai Jiao Tong University（上海交通大学）
• The University of Hong Kong（香港大学）

机构由 AI 辅助整理，请以论文原文为准。