KnotDLO: Toward Interpretable Knot Tying

• URL: http://arxiv.org/abs/2506.22176v1
• Date: Fri, 27 Jun 2025 12:43:05 GMT
• Title: KnotDLO: Toward Interpretable Knot Tying
• Authors: Holly Dinkel, Raghavendra Navaratna, Jingyi Xiang, Brian Coltin, Trey Smith, Timothy Bretl,
• Abstract summary: KnotDLO is a method for one-handed Deformable Linear Object (DLO) knot tying.<n> Grasp and target waypoints for future DLO states are planned from the current DLO shape.<n>In 16 trials of knot tying, KnotDLO achieves a 50% success rate in tying an overhand knot from previously unseen configurations.
• Score: 6.243146366966637
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This work presents KnotDLO, a method for one-handed Deformable Linear Object (DLO) knot tying that is robust to occlusion, repeatable for varying rope initial configurations, interpretable for generating motion policies, and requires no human demonstrations or training. Grasp and target waypoints for future DLO states are planned from the current DLO shape. Grasp poses are computed from indexing the tracked piecewise linear curve representing the DLO state based on the current curve shape and are piecewise continuous. KnotDLO computes intermediate waypoints from the geometry of the current DLO state and the desired next state. The system decouples visual reasoning from control. In 16 trials of knot tying, KnotDLO achieves a 50% success rate in tying an overhand knot from previously unseen configurations.

Related papers

• DLO-Splatting: Tracking Deformable Linear Objects Using 3D Gaussian Splatting [19.080636951502623]
  DLO-Splatting is an algorithm for estimating the 3D shape of Deformable Linear Objects (DLOs) from multi-view RGB images and gripper state information.<n>The algorithm uses a position-based dynamics model with shape smoothness and rigidity dampening corrections to predict the object shape.<n>Experiments demonstrate promising results in a knot tying scenario, which is challenging for existing vision-only methods.
  arXiv  Detail & Related papers  (2025-05-13T15:03:40Z)
• Online Learning and Unlearning [56.770023668379615]
  We present two online learner-unlearner (OLU) algorithms, both built upon online gradient descent (OGD)<n>The first, passive OLU, leverages OGD's contractive property and injects noise when unlearning occurs, incurring no additional computation.<n>The second, active OLU, uses an offline unlearning algorithm that shifts the model toward a solution excluding the deleted data.
  arXiv  Detail & Related papers  (2025-05-13T13:33:36Z)
• Deformable Linear Object Surface Placement Using Elastica Planning and Local Shape Control [0.0]
  This paper describes a two-layered approach for placing deformable linear objects (DLOs) on a flat surface using a single robot hand.<n>The high-level layer is a novel DLO surface placement method based on's elastica solutions.<n>The low-level layer forms a pipeline controller.
  arXiv  Detail & Related papers  (2025-03-11T15:33:36Z)
• A Distributional Treatment of Real2Sim2Real for Object-Centric Agent Adaptation in Vision-Driven Deformable Linear Object Manipulation [16.26835655544884]
  We present an integrated framework for manipulating deformable linear objects (DLOs) based on visual perception.<n>We compute the posterior distributions for the physical parameters using which we can approximately simulate the behaviour of each specific DLO.<n>We then study the implications of the resulting domain distributions in sim-based policy learning and real-world performance.
  arXiv  Detail & Related papers  (2025-02-25T20:01:06Z)
• DSLO: Deep Sequence LiDAR Odometry Based on Inconsistent Spatio-temporal Propagation [66.8732965660931]
  paper introduces a 3D point cloud sequence learning model based on inconsistent-temporal propagation for LiDAR odometry DSLO. It consists of a pyramid structure with a sequential pose module, a hierarchical pose refinement module, and a temporal feature propagation module.
  arXiv  Detail & Related papers  (2024-09-01T15:12:48Z)
• Semi-Supervised Model-Free Bayesian State Estimation from Compressed Measurements [57.04370580292727]
  We consider data-driven Bayesian state estimation from compressed measurements.<n>The dimension of the temporal measurement vector is lower than that of the temporal state vector to be estimated.<n>The underlying dynamical model of the state's evolution is unknown for a'model-free process'
  arXiv  Detail & Related papers  (2024-07-10T05:03:48Z)
• DexDLO: Learning Goal-Conditioned Dexterous Policy for Dynamic Manipulation of Deformable Linear Objects [7.72979328949568]
  We present DexDLO, a model-free framework that learns dexterous dynamic manipulation policies for deformable linear objects with a fixed-base dexterous hand. We show that our framework can efficiently learn five different DLO manipulation tasks with the same framework parameters.
  arXiv  Detail & Related papers  (2023-12-23T09:26:20Z)
• DLOFTBs -- Fast Tracking of Deformable Linear Objects with B-splines [1.3836565669337055]
  This paper proposes an algorithm for fast-tracking the shape of a DLO based on the masked image. Experiments show that our solution outperforms the State-of-the-Art approaches in DLO's shape reconstruction accuracy and algorithm running time.
  arXiv  Detail & Related papers  (2023-02-27T11:54:04Z)
• Modelling Latent Dynamics of StyleGAN using Neural ODEs [52.03496093312985]
  We learn the trajectory of independently inverted latent codes from GANs. The learned continuous trajectory allows us to perform infinite frame and consistent video manipulation. Our method achieves state-of-the-art performance but with much less computation.
  arXiv  Detail & Related papers  (2022-08-23T21:20:38Z)
• Neural Maximum A Posteriori Estimation on Unpaired Data for Motion Deblurring [87.97330195531029]
  We propose a Neural Maximum A Posteriori (NeurMAP) estimation framework for training neural networks to recover blind motion information and sharp content from unpaired data. The proposed NeurMAP is an approach to existing deblurring neural networks, and is the first framework that enables training image deblurring networks on unpaired datasets.
  arXiv  Detail & Related papers  (2022-04-26T08:09:47Z)
• IDEA-Net: Dynamic 3D Point Cloud Interpolation via Deep Embedding Alignment [58.8330387551499]
  We formulate the problem as estimation of point-wise trajectories (i.e., smooth curves) We propose IDEA-Net, an end-to-end deep learning framework, which disentangles the problem under the assistance of the explicitly learned temporal consistency. We demonstrate the effectiveness of our method on various point cloud sequences and observe large improvement over state-of-the-art methods both quantitatively and visually.
  arXiv  Detail & Related papers  (2022-03-22T10:14:08Z)
• Generalisation Guarantees for Continual Learning with Orthogonal Gradient Descent [81.29979864862081]
  In Continual Learning settings, deep neural networks are prone to Catastrophic Forgetting. We present a theoretical framework to study Continual Learning algorithms in the Neural Tangent Kernel regime.
  arXiv  Detail & Related papers  (2020-06-21T23:49:57Z)

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.