TacLoc: Global Tactile Localization on Objects from a Registration Perspective

• URL: http://arxiv.org/abs/2603.10565v1
• Date: Wed, 11 Mar 2026 09:15:07 GMT
• Title: TacLoc: Global Tactile Localization on Objects from a Registration Perspective
• Abstract summary: Pose estimation is essential for robotic manipulation, particularly when visual perception is occluded during gripper-object interactions.<n>Existing tactile-based methods rely on tactile simulation or pre-trained models, which limits their generalizability and efficiency.<n>We propose TacLoc, a novel tactile localization framework that formulates the problem as a one-shot point cloud registration task.
• Score: 8.561002414977509
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Pose estimation is essential for robotic manipulation, particularly when visual perception is occluded during gripper-object interactions. Existing tactile-based methods generally rely on tactile simulation or pre-trained models, which limits their generalizability and efficiency. In this study, we propose TacLoc, a novel tactile localization framework that formulates the problem as a one-shot point cloud registration task. TacLoc introduces a graph-theoretic partial-to-full registration method, leveraging dense point clouds and surface normals from tactile sensing for efficient and accurate pose estimation. Without requiring rendered data or pre-trained models, TacLoc achieves improved performance through normal-guided graph pruning and a hypothesis-and-verification pipeline. TacLoc is evaluated extensively on the YCB dataset. We further demonstrate TacLoc on real-world objects across two different visual-tactile sensors.

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