A Framework for Manipulating Deformable Linear Objects by Coherent Point Drift

Tian, Tao; Wang, Changhao; Tomizuka, Masayoshi

doi:10.1109/lra.2018.2852770

Cited by 60 publications

(31 citation statements)

References 17 publications

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“…Most recent research on deformable object manipulation has mainly focused on manipulating cloth items [10]- [12], ropes [13]- [15], and 3D deformable object [16]. Grasping deformable objects remains a sparsely explored research area [17] with the majority of works focusing on formulating quality metrics to quantify the goodness of a grasp [3], [4], [18], [19] or proposing control strategies [6]- [9].…”

Section: A Grasping Deformable Objectsmentioning

confidence: 99%

Deformation-Aware Data-Driven Grasp Synthesis

Lundell

Abu‐Dakka

et al. 2022

IEEE Robot. Autom. Lett.

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Grasp synthesis for 3D deformable objects remains a little-explored topic, most works aiming to minimize deformations. However, deformations are not necessarily harmfulhumans are, for example, able to exploit deformations to generate new potential grasps. How to achieve that on a robot is though an open question. This paper proposes an approach that uses object stiffness information in addition to depth images for synthesizing high-quality grasps. We achieve this by incorporating object stiffness as an additional input to a state-of-the-art deep grasp planning network. We also curate a new synthetic dataset of grasps on objects of varying stiffness using the Isaac Gym simulator for training the network. We experimentally validate and compare our proposed approach against the case where we do not incorporate object stiffness on a total of 2800 grasps in simulation and 560 grasps on a real Franka Emika Panda. The experimental results show significant improvement in grasp success rate using the proposed approach on a wide range of objects with varying shapes, sizes, and stiffnesses. Furthermore, we demonstrate that the approach can generate different grasping strategies for different stiffness values. Together, the results clearly show the value of incorporating stiffness information when grasping objects of varying stiffness. Code and video are available at: https://irobotics.aalto.fi/defggcnn.

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Section: A Grasping Deformable Objectsmentioning

confidence: 99%

Deformation-Aware Data-Driven Grasp Synthesis

Lundell

Abu‐Dakka

et al. 2022

IEEE Robot. Autom. Lett.

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“…This training set can be heavy depending on the manipulation. A learning-based approach relying on the Coherent Point Drift (CPD) method has been used both for wire tracking, task planning and trajectory planning in [11]. The CPD method permits to compute the non-rigid transformation of a point set into another one and to determine if the current tracked state of the wire is identified in a knowledge database of wire predetermined states.…”

Section: A Wire Shape Controlmentioning

confidence: 99%

Automatic Shape Control of Deformable Wires Based on Model-Free Visual Servoing

Lagneau

Krupa

Marchal

2020

IEEE Robot. Autom. Lett.

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In this paper, we propose a novel approach to automatically control the 3D shape of deformable wires using robots. Our approach proposes a novel visual feature along with a novel shape servoing method to enable dual arm manipulation of deformable wires. The visual feature relies on a geometric B-spline model and the use of Sequential Importance Resampling (SIR) particle filtering to track the 3D deformed shape of a wire over time. The shape servoing method is an adaptive modelfree method that iteratively updates the deformation Jacobian matrix using weighted least-squares minimization with sliding window and an eigenvalue-based confidence criterion. We performed several experiments on wires with different mechanical properties. The results show that our approach succeeded to control the 3D shape of various wires for many different desired deformations, while working at an interactive time. It has also been shown that the shape servoing method can be used to handle large deformations by subdividing the task in successive intermediary targets to reach. These promising results pave the way for automatic control of the 3D shapes of deformable wires in many fields such as catheter insertion in medicine or wire manipulation in industry.

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“…For the relevance in several industrial applications, the manipulation of wires and cables (DLOs) is been widely discussed their behaviour has been studied with different approaches [1], [2]. Is also been examinated their application in a DLO-in-hole problem [3], [4] or for their manipulation [5]. In the field of industrial manufacturing, some studies can be found related to the assembling of electrical harnesses [6], [7] in which the idea of using environmental contacts to shape the wire along a desired path or to place it in a certain position is exploited.…”

Section: Introductionmentioning

confidence: 99%

Robotic Wires Manipulation for Switchgear Cabling and Wiring Harness Manufacturing

Galassi

Palli

2021

2021 4th IEEE International Conference on Industrial Cyber-Physical Systems (ICPS)

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This paper describes the development of a system for a wiring operation, composed by a robotic manipulator and a gripper with tactile sensors. The manipulation of electrical wires, and more in general deformable linear objects, is a challenging task of large interest in many industrial applications. The solution studied, is designed to shape a cable along a given path with fixing points and obstacles exploited to shape the cable itself. The wire position is determined starting from a list of point and then converted to a joint reference pose for the manipulator. Moreover, tension of the wire is controlled on the basis of both the estimation of the robot external wrench and the tactile data by acting on the gripper finger opening. An experimental setup in which a cable must be routed along two linear paths connected by a turn and with four fixing points has been used to validate the proposed solution.

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A Framework for Manipulating Deformable Linear Objects by Coherent Point Drift

Cited by 60 publications

References 17 publications

Deformation-Aware Data-Driven Grasp Synthesis

Deformation-Aware Data-Driven Grasp Synthesis

Automatic Shape Control of Deformable Wires Based on Model-Free Visual Servoing

Robotic Wires Manipulation for Switchgear Cabling and Wiring Harness Manufacturing

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