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Live Thread Reconstruction for Autonomous Manipulation

Emma Huang, Omar Hernandez, Zih-Yun Chiu, Michael Yip

arXiv:2610.30694v1cs.RO

TL;DR

Suturing requires continuous tracking of a deforming thread whose configuration cannot be summarized by a fixed pose. The paper integrates live reconstruction with autonomous manipulation, improving hard-configuration needle pickup over static reconstruction while also evaluating knot tightening.

  • Problem

    A deformable surgical thread has effectively infinite degrees of freedom and must be continuously re-estimated for suturing maneuvers.

    Source: §I · p. 1

  • Method

    The pipeline combines live thread reconstruction, Kalman filtering, thread-gripper interaction modeling, and safety-constrained grasp optimization for autonomous manipulation.

    Source: §I · pp. 1–2

  • Results

    Live reconstruction achieved 14/15 (93%) hard-configuration needle pickups versus 7/10 (70%) with static reconstruction; knot tightening succeeded in 15/15 (100%) easy/medium and 13/15 (87%) hard trials.

    Source: §III · p. 3

  • Takeaways & Limitations

    The pipeline sustains thread grasping under continuous live manipulation and shows potential for varied thread manipulations.

    Source: §IV · p. 3

    The reconstruction cannot accurately represent epipolar-parallel thread segments, limiting the range of possible manipulations.

    Source: §IV · p. 3

Abstract

from arXiv · show

Autonomous robotic surgery has drawn increased attention in recent years due to its potential to relieve surgeons of time-consuming, mentally straining, and tedious tasks, particularly during prolonged operations. One suitable task for automation is suturing, which involves tracking the thread as it deforms during needle retrieval, de-tangling, knot tightening, and other tasks. Unlike rigid-object manipulation, deformable thread manipulation has no fixed pose to track and effectively infinite degrees of freedom, making its configuration difficult to predict and requiring the thread's state to be re-estimated rather than assumed. At the same time, thread manipulations involve sequential motions that depend on past and current thread contact points, which must be accurately and continuously tracked. This paper presents an image-to-manipulation pipeline that concurrently generates live thread reconstruction while conducting safe and optimized sequential manipulations to achieve long-horizon tasks.

I. INTRODUCTION

Suturing requires continuous thread tracking because the thread deforms unpredictably and cannot be represented by a fixed pose. The paper proposes live reconstruction integrated with safety-constrained manipulation and evaluates it on needle pickup and knot tightening.

I. INTRODUCTION

  • The pipeline combines initial scene reconstruction, Kalman-filtered continuous tracking, safety- and task-constrained grasp optimization, and autonomous manipulation.Source: §I · p. 1
  • Live thread reconstruction is designed to remain robust to complex intersections and occlusions.The pipeline also integrates thread-gripper interaction modeling to improve reconstruction reliability and manipulation efficiency.Source: §I · pp. 1–2
  • The pipeline is experimentally evaluated on needle pickup and knot tightening under continuous, live reconstruction.Source: §I · p. 2

A. Thread Reconstruction Overview

Each frame’s rectified stereo images and binary mask feed a five-stage reconstruction pipeline, with the EKF posterior guiding later frames.

A. Thread Reconstruction Overview

B. Keypoint Selection

Keypoint selection combines stereo depth processing with spatial filtering, while intersection detection verifies crossing geometry using local image structure and fitted thread arms.

B. Keypoint Selection

  • Stereo matching uses the center of the contiguous disparity plateau; confidence derives from the next-best score margin and plateau width.Disparities are converted to 3-D, workspace-filtered, and clustered; clusters without reliable depth interpolate Z from nearby reliable anchors using inverse graph distance.Source: B. Keypoint Selection · p. 2
  • Clusters without reliable depth interpolate Z from the two nearest reliable anchor clusters, weighted by inverse Dijkstra distance over the adjacency graph.Source: B. Keypoint Selection · p. 2

C. Intersection Detection

  • Crossing candidates pass a structure-tensor eigenvalue-ratio test, then RANSAC-fitted arms must straddle the centroid before their axes define the crossing center.Source: C. Intersection Detection · p. 2

D. Keypoint Ordering

Keypoint ordering matches current observations to the prior posterior, and the spline-state EKF updates a tool-motion-warped control polygon using confidence-weighted observations and shape priors.

D. Keypoint Ordering

  • Warm ordering matches keypoints to the previous posterior using distance, Mahalanobis fit, ambiguity, and crossing-proximity gates, with match quality qi in [0, 1].Crossing-center matches are removed and may later be interpolated along the nearest verified arm after a 3-D χ2 test.Source: D. Keypoint Ordering · p. 2

E. Initialization and Spline-State Extended Kalman Filter

  • The spline EKF represents the thread as a cubic-spline control polygon with linear observations and warps control points according to tool motion.The warp is exponentially damped by distance to the tool tip, and process noise scales with motion magnitude.Source: E. Initialization and Spline-State… · p. 2
  • Measurement precision is scaled by match quality and confidence, while smoothness, curvature, arc-length, and knot-count priors enter as pseudo-measurements.Source: E. Initialization and Spline-State… · p. 2
  • Low-support interior control points are frozen, unsupported ends are trimmed, and bounds relax as match quality falls.Source: E. Initialization and Spline-State… · p. 2

1) Needle Pick-Up:

Needle pick-up walks a grip toward the needle through repeated thread transfers, while de-tangling targets and untwists the highest-standing strand at each crossing.

1) Needle Pick-Up:

  • The active holder repeatedly lifts, pulls, pins, releases, re-centers, and regrips while the other arm anchors the thread, advancing toward the needle tip.The grasp point nearest the needle is selected by bypassing crossing predicates; the stroke lifts orthogonally to the anchor’s shaft.Source: 1) Needle Pick-Up · p. 2

2) De-tangling:

  • De-tangling targets the crossing with the greatest upper-pass height, evaluating thread-sample height along an arc band inside the loop.Grasp candidates are restricted to the loop interior and a defined arc band.Source: 2) De-tangling · p. 2
  • After lifting and rolling the strand about the shaft axis to untwist the loop, the arm withdraws along the loop tangent and uses needle-pick-up pull-through rounds until crossings are resolved.Source: 2) De-tangling · p. 2

3) Knot Tightening:

Knot tightening removes slack by restricting grasps to the hold-to-knot span and pulling along the thread while preserving tension through alternating handoffs.

3) Knot Tightening:

  • Grasp candidates are restricted to the span between the existing hold and knot, excluding loop interiors and enforcing keep-out spheres around the knot and crossings.Source: 3) Knot Tightening · p. 2
  • The pull follows the thread tangent on the needle side of the knot, skewed for horizontal progress and tilted upward to lift the knot during cinching.Source: 3) Knot Tightening · p. 2
  • Alternating trade rounds keep tension from fully dropping: the receiver grasps and closes before the giver releases, repeating until the hold reaches the knot.Source: 3) Knot Tightening · p. 2

III. EXPERIMENTS AND RESULTS

The evaluation compares static and live reconstruction for needle pick-up and tests knot tightening under live reconstruction only. Live reconstruction performs better on hard needle pick-up trials, while stability under occlusion and other conditions is reported qualitatively.

III. EXPERIMENTS AND RESULTS

  • Figure 2 showcases needle pick-up, thread de-tangling, and knot tightening, with the needle, intersection, and knot shaping manipulation motions.Source: Figure 2 · p. 3
  • 93% under live reconstruction versus 70% under static reconstruction on hard needle pick-up; live knot tightening succeeds in 15/15 (100%) easy/medium and 13/15 (87%) hard trials.Needle pick-up is comparable in easy/medium configurations; the knot-tightening evaluation uses live reconstruction only because no previous benchmark exists.Source: §III · pp. 2–3, Table I · p. 3
  • Once initialized on an unambiguous frame, reconstruction remains stable when grippers occlude thread, thread moves out of frame, or disparity becomes noisy.The authors plan to quantify stability and accuracy relative to Joglekar et al..Source: §III · p. 3
  • The listed additional evaluations include reconstruction comparisons across visibility and recovery conditions and quantitative results on further manipulation tasks, including de-tangling.Source: §III · p. 3

IV. CONCLUSION AND FUTURE WORK

The pipeline sustains thread grasping under continuous live manipulation and improves hard-configuration needle pick-up over static reconstruction. Its reconstruction remains qualitatively stable in challenging visibility conditions, but epipolar-parallel thread segments limit manipulation range.

IV. CONCLUSION AND FUTURE WORK

  • Live reconstruction improves hard-configuration needle pick-up success to 14/15 (93%) over 7/10 (70%) with static reconstruction.Source: §IV · p. 3
  • After initialization, reconstruction remains stable under gripper occlusion, out-of-frame thread motion, and noisy disparity, although these improvements still await direct quantification.The authors plan to quantify reconstruction accuracy relative to prior methods.Source: §IV · p. 3
  • Inaccurate reconstruction on epipolar-parallel thread segments limits the range of possible manipulations.The authors nevertheless describe the method as having potential for varied thread manipulations with specified criteria and constraints for surgical deployment.Source: §IV · p. 3
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