Disentangling Dense Multi-Cable Knots

• URL: http://arxiv.org/abs/2106.02252v1
• Date: Fri, 4 Jun 2021 04:31:59 GMT
• Title: Disentangling Dense Multi-Cable Knots
• Abstract summary: We formalize the problem of disentangling multiple cables and present an algorithm, Iterative Reduction Of Non-planar Multiple cAble kNots (IRON-MAN) We instantiate this algorithm with a learned perception system, inspired by prior work in single-cable untying. We experimentally evaluate the effectiveness of IRON-MAN on untangling knots of types that appear in the training data, as well as generalizing to novel classes of multi-cable knots.
• Score: 27.77670796271305
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Disentangling two or more cables requires many steps to remove crossings between and within cables. We formalize the problem of disentangling multiple cables and present an algorithm, Iterative Reduction Of Non-planar Multiple cAble kNots (IRON-MAN), that outputs robot actions to remove crossings from multi-cable knotted structures. We instantiate this algorithm with a learned perception system, inspired by prior work in single-cable untying that given an image input, can disentangle two-cable twists, three-cable braids, and knots of two or three cables, such as overhand, square, carrick bend, sheet bend, crown, and fisherman's knots. IRON-MAN keeps track of task-relevant keypoints corresponding to target cable endpoints and crossings and iteratively disentangles the cables by identifying and undoing crossings that are critical to knot structure. Using a da Vinci surgical robot, we experimentally evaluate the effectiveness of IRON-MAN on untangling multi-cable knots of types that appear in the training data, as well as generalizing to novel classes of multi-cable knots. Results suggest that IRON-MAN is effective in disentangling knots involving up to three cables with 80.5% success and generalizing to knot types that are not present during training, with cables of both distinct or identical colors.

Related papers

• Autonomously Unweaving Multiple Cables Using Visual Feedback [27.549099502088875]
  Cables are deformable and can have combinations of knots and multiple interwoven segments.<n>We propose a method that utilizes visual feedback to unweave a bundle of loosely entangled cables.<n>We experimentally demonstrate that iterating the above perception-planning-action process enables unweaving electric cables and shoelaces with an 84% success rate.
  arXiv  Detail & Related papers  (2025-12-13T21:42:45Z)
• CaRoBio: 3D Cable Routing with a Bio-inspired Gripper Fingernail [2.928964540437144]
  Two-finger grippers have the risk of over-squeezing and over-tension when grasping and guiding cables.<n>A novel eagle-inspired fingernail is designed and mounted on the fingers, which helps with cable grasping on planar surfaces.<n>We present a single-grasp end-to-end 3D cable routing framework utilizing the proposed fingernails, instead of the common pick-and-place strategy.
  arXiv  Detail & Related papers  (2025-08-13T07:25:40Z)
• Design Optimization of Three-Dimensional Wire Arrangement Considering Wire Crossings for Tendon-driven Robots [8.97800260479487]
  Tendon-driven mechanisms are useful from the perspectives of variable stiffness, redundant actuation, and lightweight design.<n>This study proposes a three-dimensional wire arrangement optimization that takes wire crossings into account.
  arXiv  Detail & Related papers  (2025-07-06T03:56:01Z)
• Tripod: Three Complementary Inductive Biases for Disentangled Representation Learning [52.70210390424605]
  In this work, we consider endowing a neural network autoencoder with three select inductive biases from the literature. In practice, however, naively combining existing techniques instantiating these inductive biases fails to yield significant benefits. We propose adaptations to the three techniques that simplify the learning problem, equip key regularization terms with stabilizing invariances, and quash degenerate incentives. The resulting model, Tripod, achieves state-of-the-art results on a suite of four image disentanglement benchmarks.
  arXiv  Detail & Related papers  (2024-04-16T04:52:41Z)
• Simultaneous linear connectivity of neural networks modulo permutation [38.833555803442835]
  Recent work has argued that permutation symmetries are only sources permuted appropriately. In contrast, the claim "strong" that for each pair of networks exists one permutation that connects it with the other networks.
  arXiv  Detail & Related papers  (2024-04-09T17:50:38Z)
• Automatic High Resolution Wire Segmentation and Removal [54.65877883437031]
  Wires and powerlines are common visual distractions that often undermine the aesthetics of photographs. We present an automatic wire clean-up system that eases the process of wire segmentation and removal/inpainting to within a few seconds.
  arXiv  Detail & Related papers  (2023-04-01T04:24:12Z)
• Autonomously Untangling Long Cables [79.09016120505088]
  Cables are ubiquitous in many settings, but are prone to self-occlusions and knots. In this paper, we focus on autonomously untangling cables up to 3 meters in length using a bilateral robot. We develop new motion primitives to efficiently untangle long cables and novel jaws specialized for this task.
  arXiv  Detail & Related papers  (2022-07-16T02:35:09Z)
• Untangling Dense Non-Planar Knots by Learning Manipulation Features and Recovery Policies [28.75186178614213]
  We present two algorithms that enhance robust cable untangling, LOKI and SPiDERMan. We find that the combination of HULK, LOKI, and SPiDERMan is able to untangle dense overhand, figure-eight, double-overhand, square, bowline, granny, stevedore, and triple-overhand knots.
  arXiv  Detail & Related papers  (2021-06-29T04:13:14Z)
• Unveiling Anomalous Edges and Nominal Connectivity of Attributed Networks [53.56901624204265]
  The present work deals with uncovering anomalous edges in attributed graphs using two distinct formulations with complementary strengths. The first relies on decomposing the graph data matrix into low rank plus sparse components to improve markedly performance. The second broadens the scope of the first by performing robust recovery of the unperturbed graph, which enhances the anomaly identification performance.
  arXiv  Detail & Related papers  (2021-04-17T20:00:40Z)
• Decoupled and Memory-Reinforced Networks: Towards Effective Feature Learning for One-Step Person Search [65.51181219410763]
  One-step methods have been developed to handle pedestrian detection and identification sub-tasks using a single network. There are two major challenges in the current one-step approaches. We propose a decoupled and memory-reinforced network (DMRNet) to overcome these problems.
  arXiv  Detail & Related papers  (2021-02-22T06:19:45Z)
• Condition Assessment of Stay Cables through Enhanced Time Series Classification Using a Deep Learning Approach [4.648677931378919]
  This study proposes a data-driven method that detects cable damage from measured cable forces by recognizing biased patterns from the intact conditions. The proposed method was tested on an in-service cable-stayed bridge with damaged stay cables.
  arXiv  Detail & Related papers  (2021-01-11T05:08:19Z)
• PlueckerNet: Learn to Register 3D Line Reconstructions [57.20244406275875]
  This paper proposes a neural network based method to solve the problem of Aligning two partially-overlapped 3D line reconstructions in Euclidean space. Experiments on both indoor and outdoor datasets show that the registration (rotation and translation) precision of our method outperforms baselines significantly.
  arXiv  Detail & Related papers  (2020-12-02T11:31:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.