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This research advances robotic manipulation by enabling robots to reconstruct tool-tip contact trajectories using only grasp-level tactile sensing, a critical capability for handling dynamic interactions where the sensor is not at the point of contact. By framing this as a geometric inference problem, the study demonstrates consistent performance across varied tools and wrist poses with low error margins in both position and shape recovery. The work represents a significant step toward more robust autonomous agents capable of precise tool-mediated tasks without requiring direct sensing at every interaction point.
Geometric Reconstruction of Extrinsic Contact Trajectories using Tactile Sensing and Proprioception for Tool Manipulation
arXiv:2606.22251v2 Announce Type: replace Abstract: Tactile sensing enables robots to perceive rich contact information at the grasp, supporting tasks such as object recognition, in-hand pose estimation, and slip detection. However, in many tool-mediated manipulation tasks, the interaction that determines task success occurs at the tool tip, away from the tactile sensor, making direct sensing of tool-environment contact difficult, particularly when the contact moves during interaction. In this work, we reconstruct the trajectory of extrinsic tool-tip contact using tactile sensing and robot proprioception. We formulate tool-tip trajectory reconstruction as a geometric inference problem under a single-point contact assumption. Our method first estimates the global tool-tip contact location from a calibration segment designed to approximate fixed-point behavior, and then reconstructs the full trajectory by composing relative tool motion estimated from tactile marker observations under continuous contact. Across n=51 trials with multiple trajectories, tools, wrist poses, and grasp configurations, the proposed pipeline achieves a trajectory RMSE of 8.59 +/- 2.41 mm in the world frame and a shape RMSE of 5.96 +/- 1.16 mm, while operating online at 14.00 +/- 4.11 Hz. Overall, the results show that extrinsic tool-tip trajectory geometry can be recovered consistently from grasp-level tactile sensing, with trajectory shape remaining stable across variations in tools, wrist poses, and grasp configurations.
Abstract:Tactile sensing enables robots to perceive rich contact information at the grasp, supporting tasks such as object recognition, in-hand pose estimation, and slip detection. However, in many tool-mediated manipulation tasks, the interaction that determines task success occurs at the tool tip, away from the tactile sensor, making direct sensing of tool-environment contact difficult, particularly when the contact moves during interaction. In this work, we reconstruct the trajectory of extrinsic tool-tip contact using tactile sensing and robot proprioception. We formulate tool-tip trajectory reconstruction as a geometric inference problem under a single-point contact assumption. Our method first estimates the global tool-tip contact location from a calibration segment designed to approximate fixed-point behavior, and then reconstructs the full trajectory by composing relative tool motion estimated from tactile marker observations under continuous contact. Across n=51 trials with multiple trajectories, tools, wrist poses, and grasp configurations, the proposed pipeline achieves a trajectory RMSE of 8.59 +/- 2.41 mm in the world frame and a shape RMSE of 5.96 +/- 1.16 mm, while operating online at 14.00 +/- 4.11 Hz. Overall, the results show that extrinsic tool-tip trajectory geometry can be recovered consistently from grasp-level tactile sensing, with trajectory shape remaining stable across variations in tools, wrist poses, and grasp configurations.
Accepted to the 2026 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2026). Updated to the final conference version
Robotics (cs.RO)
arXiv:2606.22251 [cs.RO]
(or arXiv:2606.22251v2 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2606.22251 Focus to learn more arXiv-issued DOI via DataCite
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