LaSeSOM: A Latent and Semantic Representation Framework for Soft Object Manipulation

Zhou, Peng; Zhu, Jihong; Huo, Shengzeng; Navarro-Alarcon, David

doi:10.1109/lra.2021.3074872

Cited by 21 publications

(8 citation statements)

References 44 publications

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“…An intuitive way to automatically drive the agents and distribute them uniformly along the boundary of interest is to parameterize it by its arc length [27]. However, a very accurate characterization of the observed contour (1) may require a large number of frequency terms, which invariably leads to complicated (and high dimensional) geometric representations.…”

Section: B Curve Approximationmentioning

confidence: 99%

Fourier-Based Multi-Agent Formation Control to Track Evolving Closed Boundaries

Zhang¹,

Zhi²,

Romero³

et al. 2023

Preprint

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<p>The automatic monitoring/tracking of environmental boundaries by multi-agent systems is a fundamental problem which has many practical applications. In this paper, we address this problem with formation control techniques based on parametric curves that represent the boundary’s feedback shape. For that, we approximate the curve with truncated Fourier series, whose finite coefficients are utilized to characterize the curve’s shape and to automatically distribute the agents along it. These feedback Fourier coefficients are exploited to design a new type of formation controller that drives the agents to form desired curves. A detailed stability analysis is provided for the proposed control methodology, considering both fixed and switching multi-agent topologies. The reported numerical simulation study demonstrates the performance and feasibility of our new method to track closed boundaries of different shapes.</p>

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Section: B Curve Approximationmentioning

confidence: 99%

Fourier-Based Multi-Agent Formation Control to Track Evolving Closed Boundaries

Zhang¹,

Zhi²,

Romero³

et al. 2023

Preprint

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“…The aforementioned point-cloud-based works require additional processing of the noisy point clouds, such as nonrigid registrations (Jin et al (2019)), occlusion removal (Hu et al (2019)), re-samplings (Lagneau et al (2020a); Zhou et al (2021); Thach et al (2022)), correspondence identification (Shen et al (2022)), and surface reconstructions/refinements (Shi et al (2022)). In comparison, our method can directly extract deformation features from raw point clouds without extra point processing.…”

Section: Shape Control Of Deformable Objectsmentioning

confidence: 99%

Modal-graph 3D shape servoing of deformable objects with raw point clouds

Yang,

Sui,

Zhong

et al. 2023

The International Journal of Robotics Research

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Deformable object manipulation (DOM) with point clouds has great potential as nonrigid 3D shapes can be measured without detecting and tracking image features. However, robotic shape control of deformable objects with point clouds is challenging due to: the unknown point correspondences and the noisy partial observability of raw point clouds; the modeling difficulties of the relationship between point clouds and robot motions. To tackle these challenges, this paper introduces a novel modal-graph framework for the model-free shape servoing of deformable objects with raw point clouds. Unlike the existing works studying the object’s geometry structure, we propose a modal graph to describe the low-frequency deformation structure of the DOM system, which is robust to the measurement irregularities. The modal graph enables us to directly extract low-dimensional deformation features from raw point clouds without extra processing of registrations, refinements, and occlusion removal. It also preserves the spatial structure of the DOM system to inverse the feature changes into robot motions. Moreover, as the framework is built with unknown physical and geometric object models, we design an adaptive robust controller to deform the object toward the desired shape while tackling the modeling uncertainties, noises, and disturbances online. The system is proved to be input-to-state stable (ISS) using Lyapunov-based methods. Extensive experiments are conducted to validate our method using linear, planar, tubular, and volumetric objects under different settings.

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“…Machine Learning is also employed to solve planning tasks, Tanaka et al [34] and Yang et al [35] proposed a manipulation planning method for cloth by using the neural network. More recently, [36] learns a latent representation for semantic soft object manipulation that enables (quasi) shape planning with deformable objects.…”

Section: Planningmentioning

confidence: 99%

Challenges and Outlook in Robotic Manipulation of Deformable Objects

Zhu

Cherubini

Dune

et al. 2022

IEEE Robot. Automat. Mag.

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Deformable object manipulation (DOM) is an emerging research problem in robotics. The ability to manipulate deformable objects endows robots with higher autonomy and promises new applications in the industrial, services, and healthcare sectors. However, compared to rigid object manipulation, the manipulation of deformable objects is considerably more complex, and is still an open research problem. Tackling the challenges in DOM demands breakthroughs in almost all aspects of robotics, namely hardware design, sensing, deformation modeling, planning, and control. In this article, we highlight the main challenges that arise by considering deformation and review recent advances in each sub-field. A particular focus of our paper lies in the discussions of these challenges and proposing promising directions of research.

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LaSeSOM: A Latent and Semantic Representation Framework for Soft Object Manipulation

Cited by 21 publications

References 44 publications

Fourier-Based Multi-Agent Formation Control to Track Evolving Closed Boundaries

Fourier-Based Multi-Agent Formation Control to Track Evolving Closed Boundaries

Modal-graph 3D shape servoing of deformable objects with raw point clouds

Challenges and Outlook in Robotic Manipulation of Deformable Objects

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