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arxiv: 2606.22251 · v1 · pith:5XFRAD6Snew · submitted 2026-06-20 · 💻 cs.RO

Geometric Reconstruction of Extrinsic Contact Trajectories using Tactile Sensing and Proprioception for Tool Manipulation

Seojung Min , Yoonjin Kim , Jeong-Jung Kim , Jung Kim This is my paper

Pith reviewed 2026-06-26 11:28 UTC · model grok-4.3

classification 💻 cs.RO
keywords tactile sensingproprioceptiontrajectory reconstructiontool manipulationextrinsic contactgeometric inferencesingle-point contact
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The pith

Grasp-level tactile sensing combined with proprioception reconstructs tool-tip contact trajectories under a single-point contact model.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper establishes that the geometry of extrinsic tool-tip trajectories can be recovered from tactile observations at the grasp together with the robot's proprioceptive joint data. It formulates the task as geometric inference that first fixes an initial world-frame location using a calibration segment and then assembles the full path by composing relative motions extracted from tactile marker displacements while contact persists. A sympathetic reader would care because many tool-mediated tasks succeed or fail based on the precise path of the distant tip, yet sensors are typically mounted only at the gripper. The reported results indicate that shape remains consistent across different tools, wrist orientations, and grasp poses while running online.

Core claim

Tool-tip trajectory reconstruction is formulated as a geometric inference problem under a single-point contact assumption. The global tool-tip contact location is estimated from a calibration segment designed to approximate fixed-point behavior, after which the full trajectory is reconstructed by composing relative tool motions estimated from tactile marker observations under continuous contact.

What carries the argument

Composition of relative tool motions from tactile marker observations, initialized by global location from a calibration segment under the single-point contact assumption.

If this is right

  • Extrinsic tool-tip trajectory geometry can be recovered consistently from grasp-level tactile sensing.
  • Trajectory shape remains stable across variations in tools, wrist poses, and grasp configurations.
  • The pipeline runs online at roughly 14 Hz while delivering millimeter-scale accuracy on both full trajectory and shape.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same geometric composition could support closed-loop control of tool paths in tasks such as surface tracing without requiring sensors at the tip.
  • Relaxing the single-point assumption to allow brief multi-point episodes might extend the method to more complex contacts while preserving the calibration step.
  • Because shape recovery proved robust to grasp changes, the approach may transfer to compliant or re-grasping scenarios common in unstructured environments.

Load-bearing premise

Contact remains a single point at all times and a calibration segment can approximate fixed-point behavior well enough to initialize absolute location in the world frame.

What would settle it

Running the pipeline on a continuous curved-surface contact task while recording ground-truth tip position with an external optical tracker and finding average world-frame RMSE substantially larger than 8.59 mm.

Figures

Figures reproduced from arXiv: 2606.22251 by Jeong-Jung Kim, Jung Kim, Seojung Min, Yoonjin Kim.

Figure 1
Figure 1. Figure 1: Experimental setup and interaction scenario. (a) Overall system setup, (b) Grooved structure defining the ground-truth trajectories (top view and cross-section), (c) Stage I: the end-effector moves slightly while the tool tip remains fixed, and (d) Stage II: the end-effector moves straight while the tool tip follows the groove. estimation using tactile geometry and proprioception [12]– [16], and slip or sh… view at source ↗
Figure 2
Figure 2. Figure 2: Two-stage reconstruction pipeline. We estimate per-frame 6-DoF relative tool motion (Rt ,tt) from dual tactile marker trajectories and anchor the initial tool-tip contact point pˆB(0) using a fixed-point calibration segment (Stage I). The full tool-tip trajectory pˆB(t) is then reconstructed by propagating the anchored point through the estimated motions (Stage II). by exploiting consistency between tactil… view at source ↗
Figure 3
Figure 3. Figure 3: Geometric principle of the two-stage reconstruction. (a) Stage I (anchoring): during calibration, the tool tip is approximately stationary in the base/world frame, yielding fixed-point constraints that estimate the initial contact pˆB(0). (b) Stage II (propagation): the anchored point is propagated through the estimated relative rigid motions (Rt ,tt) from tactile markers to reconstruct pˆB(t). Signals: At… view at source ↗
Figure 4
Figure 4. Figure 4: Experimental conditions. We evaluate robustness across variations in (a) trajectory (where two numbers of sinusoids denote period and amplitude, respectively), (b) tool geometry, (c) wrist pose (where the three angles stand for Euler angles in the robot base frame), and (d) grasp configuration. III. EXPERIMENTS We evaluate the proposed pipeline where the tool tip inter￾acts with a planar grooved trajectori… view at source ↗
Figure 5
Figure 5. Figure 5: Qualitative trajectory reconstruction. Reconstructed trajectories (blue) are overlaid across trials and compared with ground-truth trajectories (black) under representative condition sets. TABLE II OVERALL RECONSTRUCTION PERFORMANCE (n = 51). Metric Mean ± Std Trajectory RMSE (world frame) [mm] 8.59±2.41 Shape RMSE (initial-point aligned) [mm] 5.96±1.16 Initial contact error [mm] 9.48±3.77 Loop rate [Hz] 1… view at source ↗
Figure 6
Figure 6. Figure 6: Error decomposition. Trajectory RMSE (world frame) correlates strongly with initial contact error, whereas shape RMSE shows weak correlation, indicating that global anchoring bias dominates absolute error. A B C D Condition set 0 3.4 6.7 10.1 Shape RMSE [mm] [PITH_FULL_IMAGE:figures/full_fig_p007_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: shows the distribution of shape RMSE across condition sets. Despite these variations, the mean shape error remains within 5.0–6.6 mm across all sets. This rel￾atively narrow range suggests that the proposed reconstruc￾tion pipeline is not strongly sensitive to these experimental factors. In particular, variations in trajectory shape do not lead to systematic degradation in reconstruction accuracy. Both smo… view at source ↗
read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper claims to reconstruct the trajectory of extrinsic tool-tip contact in tool-mediated manipulation using grasp-level tactile sensing and robot proprioception. Under a single-point contact assumption, the method first estimates the global tool-tip location from a calibration segment designed to approximate fixed-point behavior, then reconstructs the full trajectory by composing relative tool motions recovered from tactile marker observations during continuous contact. Across 51 trials varying trajectories, tools, wrist poses, and grasp configurations, it reports a world-frame trajectory RMSE of 8.59 ± 2.41 mm, shape RMSE of 5.96 ± 1.16 mm, and online operation at 14.00 ± 4.11 Hz, concluding that tip trajectory geometry can be recovered consistently from grasp sensing.

Significance. If the geometric reconstruction holds under the stated assumptions, the work provides a practical way to infer distant tool-environment contact from proximal tactile sensors, which is relevant for robotic manipulation tasks where direct tip sensing is unavailable. The multi-condition experimental results (n=51) and reported online frequency demonstrate consistency of trajectory shape recovery, which could support downstream applications in tool use if the load-bearing assumptions are validated.

major comments (2)
  1. [Abstract / Method] Abstract and Method (implied in the geometric inference formulation): The single-point contact assumption and the calibration-segment fixed-point approximation are load-bearing for the composition of relative motions, yet the manuscript provides no independent verification (e.g., via ground-truth contact imaging or force-torque analysis) that these hold during the continuous-contact phase; violation would introduce unrecoverable offset or drift in the integrated trajectory.
  2. [Experiments] Experiments section: While the n=51 trials support the reported RMSE values under the tested conditions, there is no ablation or failure-case analysis for scenarios where contact deviates from a single point (e.g., rolling or compliance) or where the calibration segment does not approximate fixed-point behavior, leaving the robustness of the central claim untested.
minor comments (2)
  1. [Abstract] The abstract states performance metrics but omits any derivation details, error-propagation analysis, or explicit handling of contact loss; adding these would improve verifiability without altering the core contribution.
  2. [Method] Notation for the relative-motion composition and marker observations could be clarified with an explicit equation or diagram in the method description to aid reproducibility.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive comments. We address each major comment below.

read point-by-point responses

  1. Referee: [Abstract / Method] The single-point contact assumption and the calibration-segment fixed-point approximation are load-bearing for the composition of relative motions, yet the manuscript provides no independent verification (e.g., via ground-truth contact imaging or force-torque analysis) that these hold during the continuous-contact phase; violation would introduce unrecoverable offset or drift in the integrated trajectory.

    Authors: We agree the single-point contact assumption and fixed-point calibration approximation are central to the geometric composition of relative motions, as formulated in the method. The calibration segment is designed specifically to enable global tip localization under this approximation. Our n=51 trials demonstrate consistent reconstruction performance, but we acknowledge the absence of independent verification (such as force-torque or imaging data) during continuous contact. In revision we will add an explicit paragraph in the Discussion section stating the assumptions, their role in preventing drift, and the conditions under which violation could affect results. revision: partial

  2. Referee: [Experiments] While the n=51 trials support the reported RMSE values under the tested conditions, there is no ablation or failure-case analysis for scenarios where contact deviates from a single point (e.g., rolling or compliance) or where the calibration segment does not approximate fixed-point behavior, leaving the robustness of the central claim untested.

    Authors: The 51 trials systematically vary trajectories, tools, wrist poses, and grasp configurations while remaining within the single-point regime, supporting the reported RMSE and shape metrics. We did not include explicit ablations or failure cases for multi-point contact or non-ideal calibration because the work centers on validating the inference pipeline under the stated assumptions. In revision we will add a limitations subsection to the Experiments and Discussion sections that discusses these scenarios and identifies them as future work. revision: yes

Circularity Check

0 steps flagged

No circularity; derivation is self-contained geometric composition

full rationale

The paper presents a geometric inference pipeline: calibrate global tool-tip location from a fixed-point approximation segment, then compose relative motions recovered from tactile marker observations under a single-point contact assumption. Reported RMSE values are computed against independent external observations (world-frame and shape metrics). No equations, parameters, or steps reduce by construction to fitted inputs, self-definitions, or self-citation chains; the calibration segment and marker data remain independent of the final performance numbers. This matches the default expectation of no significant circularity.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The approach rests on the single-point contact assumption and the calibration approximating fixed-point behavior; no free parameters, invented entities, or additional axioms are identifiable from the abstract.

axioms (2)
  • domain assumption single-point contact assumption
    Formulate tool-tip trajectory reconstruction as a geometric inference problem under a single-point contact assumption.
  • domain assumption calibration segment approximates fixed-point behavior
    Estimates the global tool-tip contact location from a calibration segment designed to approximate fixed-point behavior.

pith-pipeline@v0.9.1-grok · 5780 in / 1344 out tokens · 25485 ms · 2026-06-26T11:28:04.991348+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

33 extracted references

  1. [1]

    Gelsight: High-resolution robot tactile sensors for estimating geometry and force,

    W. Yuan, S. Dong, and E. H. Adelson, “Gelsight: High-resolution robot tactile sensors for estimating geometry and force,”Sensors, vol. 17, no. 12, p. 2762, 2017

    2017

  2. [2]

    Digit: A novel design for a low-cost compact high-resolution tactile sensor with application to in-hand manipulation,

    M. Lambeta, P.-W. Chou, S. Tian, B. Yang, B. Maloon, V . R. Most, D. Stroud, R. Santos, A. Byagowi, G. Kammereret al., “Digit: A novel design for a low-cost compact high-resolution tactile sensor with application to in-hand manipulation,”IEEE Robotics and Automation Letters, vol. 5, no. 3, pp. 3838–3845, 2020

    2020

  3. [3]

    The tactip family: Soft optical tactile sensors with 3d-printed biomimetic morphologies,

    B. Ward-Cherrier, N. Pestell, L. Cramphorn, B. Winstone, M. E. Giannaccini, J. Rossiter, and N. F. Lepora, “The tactip family: Soft optical tactile sensors with 3d-printed biomimetic morphologies,”Soft robotics, vol. 5, no. 2, pp. 216–227, 2018

    2018

  4. [4]

    Gelslim 3.0: High-resolution measurement of shape, force and slip in a compact tactile-sensing finger,

    I. H. Taylor, S. Dong, and A. Rodriguez, “Gelslim 3.0: High-resolution measurement of shape, force and slip in a compact tactile-sensing finger,” in2022 International Conference on Robotics and Automation (ICRA). IEEE, 2022, pp. 10 781–10 787

    2022

  5. [5]

    9dtact: A compact vision-based tactile sensor for accurate 3d shape reconstruction and generalizable 6d force estimation,

    C. Lin, H. Zhang, J. Xu, L. Wu, and H. Xu, “9dtact: A compact vision-based tactile sensor for accurate 3d shape reconstruction and generalizable 6d force estimation,”IEEE Robotics and Automation Letters, vol. 9, no. 2, pp. 923–930, 2023

    2023

  6. [6]

    Tactile robotics: An outlook,

    S. Luo, N. F. Lepora, W. Yuan, K. Althoefer, G. Cheng, and R. Dahiya, “Tactile robotics: An outlook,”IEEE Transactions on Robotics, 2025

    2025

  7. [7]

    Classification of vision-based tactile sensors: A review,

    H. Li, Y . Lin, C. Lu, M. Yang, E. Psomopoulou, and N. F. Lepora, “Classification of vision-based tactile sensors: A review,”IEEE Sen- sors Journal, 2025

    2025

  8. [8]

    Single- grasp object classification and feature extraction with simple robot hands and tactile sensors,

    A. J. Spiers, M. V . Liarokapis, B. Calli, and A. M. Dollar, “Single- grasp object classification and feature extraction with simple robot hands and tactile sensors,”IEEE transactions on haptics, vol. 9, no. 2, pp. 207–220, 2016

    2016

  9. [9]

    Iterative closest labeled point for tactile object shape recognition,

    S. Luo, W. Mou, K. Althoefer, and H. Liu, “Iterative closest labeled point for tactile object shape recognition,” in2016 IEEE/RSJ Interna- tional Conference on Intelligent Robots and Systems (IROS). IEEE, 2016, pp. 3137–3142

    2016

  10. [10]

    Shape-independent hardness estimation using deep learning and a gelsight tactile sensor,

    W. Yuan, C. Zhu, A. Owens, M. A. Srinivasan, and E. H. Adelson, “Shape-independent hardness estimation using deep learning and a gelsight tactile sensor,” in2017 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2017, pp. 951–958

    2017

  11. [11]

    Vitac: Feature sharing between vision and tactile sensing for cloth texture recognition,

    S. Luo, W. Yuan, E. Adelson, A. G. Cohn, and R. Fuentes, “Vitac: Feature sharing between vision and tactile sensing for cloth texture recognition,” in2018 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2018, pp. 2722–2727

    2018

  12. [12]

    In-hand object pose estimation using covariance-based tactile to geometry matching,

    J. Bimbo, S. Luo, K. Althoefer, and H. Liu, “In-hand object pose estimation using covariance-based tactile to geometry matching,”IEEE Robotics and Automation Letters, vol. 1, no. 1, pp. 570–577, 2016

    2016

  13. [13]

    Tactile object pose estimation from the first touch with geometric contact rendering,

    M. B. Villalonga, A. Rodriguez, B. Lim, E. Valls, and T. Sechopoulos, “Tactile object pose estimation from the first touch with geometric contact rendering,” inConference on Robot Learning. PMLR, 2021, pp. 1015–1029

    2021

  14. [14]

    Tac2pose: Tactile object pose estimation from the first touch,

    M. Bauza, A. Bronars, and A. Rodriguez, “Tac2pose: Tactile object pose estimation from the first touch,”The International Journal of Robotics Research, vol. 42, no. 13, pp. 1185–1209, 2023

    2023

  15. [15]

    Tactile pose estimation and policy learning for unknown object manipulation,

    T. Kelestemur, R. Platt, and T. Padir, “Tactile pose estimation and policy learning for unknown object manipulation,” inProceedings of the 21st International Conference on Autonomous Agents and Multiagent Systems, 2022, pp. 742–750

    2022

  16. [16]

    In- hand object classification and pose estimation with sim-to-real tactile transfer for robotic manipulation,

    S. Yang, W. D. Kim, H. Park, S. Min, H. Han, and J. Kim, “In- hand object classification and pose estimation with sim-to-real tactile transfer for robotic manipulation,”IEEE Robotics and Automation Letters, vol. 9, no. 1, pp. 659–666, 2023

    2023

  17. [17]

    Pose-and-shear-based tactile servoing,

    J. Lloyd and N. F. Lepora, “Pose-and-shear-based tactile servoing,” The International Journal of Robotics Research, vol. 43, no. 7, pp. 1024–1055, 2024

    2024

  18. [18]

    Shear-based grasp control for multi-fingered underactuated tactile robotic hands,

    C. J. Ford, H. Li, M. G. Catalano, M. Bianchi, E. Psomopoulou, and N. F. Lepora, “Shear-based grasp control for multi-fingered underactuated tactile robotic hands,”IEEE Transactions on Robotics, 2025

    2025

  19. [19]

    Tactile control for object tracking and dynamic contour following,

    K. Aquilina, D. A. Barton, and N. F. Lepora, “Tactile control for object tracking and dynamic contour following,”Robotics and Autonomous Systems, vol. 178, p. 104710, 2024

    2024

  20. [20]

    Tactile tool manipulation,

    Y . Shirai, D. K. Jha, A. U. Raghunathan, and D. Hong, “Tactile tool manipulation,” in2023 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2023, pp. 12 597–12 603

    2023

  21. [21]

    Tactile-driven dex- terous in-hand writing via extrinsic contact sensing,

    C. Zhao, L. Xie, B. Huang, S. Wang, and D. Ma, “Tactile-driven dex- terous in-hand writing via extrinsic contact sensing,”IEEE Robotics and Automation Letters, 2025

    2025

  22. [22]

    Perceiving extrinsic contacts from touch improves learning insertion policies,

    C. Higuera, J. Ortiz, H. Qi, L. Pineda, B. Boots, and M. Mukadam, “Perceiving extrinsic contacts from touch improves learning insertion policies,”arXiv preprint arXiv:2309.16652, 2023

    arXiv 2023

  23. [23]

    Extrinsic contact sensing with relative-motion tracking from distributed tactile measurements,

    D. Ma, S. Dong, and A. Rodriguez, “Extrinsic contact sensing with relative-motion tracking from distributed tactile measurements,” in 2021 IEEE international conference on robotics and automation (ICRA). IEEE, 2021, pp. 11 262–11 268

    2021

  24. [24]

    Active extrinsic contact sensing: Ap- plication to general peg-in-hole insertion,

    S. Kim and A. Rodriguez, “Active extrinsic contact sensing: Ap- plication to general peg-in-hole insertion,” in2022 International Conference on Robotics and Automation (ICRA). IEEE, 2022, pp. 10 241–10 247

    2022

  25. [25]

    Simul- taneous tactile estimation and control of extrinsic contact,

    S. Kim, D. K. Jha, D. Romeres, P. Patre, and A. Rodriguez, “Simul- taneous tactile estimation and control of extrinsic contact,” in2023 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2023, pp. 12 563–12 569

    2023

  26. [26]

    Neural contact fields: Tracking extrinsic contact with tactile sensing,

    C. Higuera, S. Dong, B. Boots, and M. Mukadam, “Neural contact fields: Tracking extrinsic contact with tactile sensing,”arXiv preprint arXiv:2210.09297, 2022

    arXiv 2022

  27. [27]

    Simultaneous contact location and object pose estimation using proprioception and tactile feedback,

    A. Sipos and N. Fazeli, “Simultaneous contact location and object pose estimation using proprioception and tactile feedback,” in2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2022, pp. 3233–3240

    2022

  28. [28]

    Simultaneous extrinsic contact and in-hand pose estimation via distributed tactile sensing,

    M. Van der Merwe, K. Ota, D. Berenson, N. Fazeli, and D. K. Jha, “Simultaneous extrinsic contact and in-hand pose estimation via distributed tactile sensing,”IEEE Robotics and Automation Letters, vol. 11, no. 3, pp. 2394–2401, 2026

    2026

  29. [29]

    Object extrinsic contact surface reconstruction through extrinsic contact sensing from visuo-tactile measurements,

    Y . Kim, W. D. Kim, and J. Kim, “Object extrinsic contact surface reconstruction through extrinsic contact sensing from visuo-tactile measurements,” in2025 IEEE/RSJ International Conference on Intel- ligent Robots and Systems (IROS). IEEE, 2025, pp. 20 968–20 973

    2025

  30. [30]

    Manipulation of unknown objects via contact configuration regulation,

    N. Doshi, O. Taylor, and A. Rodriguez, “Manipulation of unknown objects via contact configuration regulation,” in2022 International Conference on Robotics and Automation (ICRA). IEEE, 2022, pp. 2693–2699

    2022

  31. [31]

    Texterity: Tactile ex- trinsic dexterity,

    A. Bronars, S. Kim, P. Patre, and A. Rodriguez, “Texterity: Tactile ex- trinsic dexterity,” in2024 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2024, pp. 7976–7983

    2024

  32. [32]

    Tactile-driven non-prehensile object manipulation via extrinsic contact mode control

    M. Oller, D. Berenson, and N. Fazeli, “Tactile-driven non-prehensile object manipulation via extrinsic contact mode control.”

  33. [33]

    A solution for the best rotation to relate two sets of vectors,

    W. Kabsch, “A solution for the best rotation to relate two sets of vectors,”F oundations of Crystallography, vol. 32, no. 5, pp. 922–923, 1976

    1976