Untangling Multiple Deformable Linear Objects in Unknown Quantities With Complex Backgrounds

Huang, Xuzhao; Chen, Dayuan; Guo, Yuhao; Jiang, Xin; Liu, Yun-Hui

doi:10.1109/tase.2023.3233949

Cited by 10 publications

(2 citation statements)

References 28 publications

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“…Viswanath et al [38] proposed to disentangle multi-cable knots by taskrelevant keypoint prediction and knot graph representation. Huang et al [39] presented a method for untangling multiple deformable linear objects by tracing the topological representation. Other studies focus on scenes with a higher degree of entanglmenet in food industry.…”

Section: Manipulation Of Multiple Deformable Objectsmentioning

confidence: 99%

Learning Efficient Policies for Picking Entangled Wire Harnesses: An Approach to Industrial Bin Picking

Zhang

Domae

Wan

et al. 2023

IEEE Robot. Autom. Lett.

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Industrial bin picking for tangled-prone objects requires the robot to either pick up untangled objects or perform separation manipulation when the bin contains no isolated objects. The robot must be able to flexibly perform appropriate actions based on the current observation. It is challenging due to high occlusion in the clutter, elusive entanglement phenomena, and the need for skilled manipulation planning. In this paper, we propose an autonomous, effective and general approach for picking up tangled-prone objects for industrial bin picking. First, we learn PickNet -a network that maps the visual observation to pixel-wise possibilities of picking isolated objects or separating tangled objects and infers the corresponding grasp. Then, we propose two effective separation strategies: Dropping the entangled objects into a buffer bin to reduce the degree of entanglement; Pulling to separate the entangled objects in the buffer bin planned by PullNet -a network that predicts position and direction for pulling from visual input. To efficiently collect data for training PickNet and PullNet, we embrace the self-supervised learning paradigm using an algorithmic supervisor in a physics simulator. Realworld experiments show that our policy can dexterously pick up tangled-prone objects with success rates of 90%. We further demonstrate the generalization of our policy by picking a set of unseen objects. Supplementary material, code, and videos can be found at https://xinyiz0931.github.io/tangle.

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Section: Manipulation Of Multiple Deformable Objectsmentioning

confidence: 99%

Learning Efficient Policies for Picking Entangled Wire Harnesses: An Approach to Industrial Bin Picking

Zhang

Domae

Wan

et al. 2023

IEEE Robot. Autom. Lett.

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“…For instance, Nguyen et al [20] propose a Deep Learning-based data processing pipeline for automated optical inspection of wiring harnesses using real and synthetic point clouds. Additionally, Huang et al [21] present a solution based on Deep Learning that includes a strategy for untangling deformable linear objects. Even so, the lack of general wire harness datasets for the model generation is an obstacle to the application of these techniques although several works propose solutions to speed up the creation of datasets [22,23].…”

Section: Related Workmentioning

confidence: 99%

CAD-Based Robot Programming Solution for Wire Harness Manufacturing in Aeronautic Sector

González Huarte,

Ortiz de Zarate,

Ibarguren

2023

Robotics

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Wire harness manufacturing in the aeronautic sector is highly manual work, with production defined by multiple references and small batches. Although complete automation of the production process is not feasible, a robot-assisted approach could increase the efficiency of the existing production means. This paper presents a novel dual-arm robotic solution for workbench configuration and cable routing during the initial steps of wire harness manufacturing. Based on the CAD information of the wire harness, the proposed framework generates trajectories in real-time to complete the initial manufacturing tasks, dividing automatically the whole job between both robots. The presented approach has been validated in a production environment using different wire harness references, obtaining promising results and metrics.

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Rearranging Deformable Linear Objects for Implicit Goals with Self‐Supervised Planning and Control

Huo,

Hu,

Wang

et al. 2024

Advanced Intelligent Systems

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The robotic manipulation of deformable linear objects is a frontier problem with many potential applications in diverse industries. However, most existing research in this area focuses on shape control for a provided explicit goal and does not consider physical constraints, which limits its applicability in many real‐world scenarios. In this study, a self‐supervised planning and control approach are proposed to address the challenge of rearranging deformable linear objects for implicit goals. Specifically, the context of making both ends of the object reachable (inside the robotic access range) and graspable (outside potential collision regions) by dual‐arm robots is considered. Firstly, the object is described with sequential keypoints and the correspondence‐based action is parameterized. Secondly, a generator capable of producing multiple explicit targets is developed, which adhere to implicit conditions. Thirdly, value models are learnt to assign the most promising explicit target as guidance and determine the goal‐conditioned action. All models within the policy are trained in a self‐supervised manner based on data collected from simulations. Importantly, the learned policy can be directly applied to real‐world settings since we do not rely on accurate dynamic models. The performance of the new method is validated with simulations and real‐world experiments.

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Untangling Multiple Deformable Linear Objects in Unknown Quantities With Complex Backgrounds

Cited by 10 publications

References 28 publications

Learning Efficient Policies for Picking Entangled Wire Harnesses: An Approach to Industrial Bin Picking

Learning Efficient Policies for Picking Entangled Wire Harnesses: An Approach to Industrial Bin Picking

CAD-Based Robot Programming Solution for Wire Harness Manufacturing in Aeronautic Sector

Rearranging Deformable Linear Objects for Implicit Goals with Self‐Supervised Planning and Control

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