Toward Real-Time 3D Shape Tracking of Deformable Objects for Robotic Manipulation and Shape Control

Valencia, Angel J.; Nadon, Felix; Payeur, Pierre

doi:10.1109/sensors43011.2019.8956623

Cited by 7 publications

(5 citation statements)

References 13 publications

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“…Third, we test the combination of the shape estimation and prediction methods as a learned model of deformable objects in real robotic environments. This paper represents a significant extension to previous work (Valencia et al, 2019 ) that corroborates the learned model across different types of deformable objects with experimental evaluations.…”

Section: Introductionsupporting

confidence: 81%

Combining Self-Organizing and Graph Neural Networks for Modeling Deformable Objects in Robotic Manipulation

Valencia

Payeur

2020

Front. Robot. AI

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Modeling deformable objects is an important preliminary step for performing robotic manipulation tasks with more autonomy and dexterity. Currently, generalization capabilities in unstructured environments using analytical approaches are limited, mainly due to the lack of adaptation to changes in the object shape and properties. Therefore, this paper proposes the design and implementation of a data-driven approach, which combines machine learning techniques on graphs to estimate and predict the state and transition dynamics of deformable objects with initially undefined shape and material characteristics. The learned object model is trained using RGB-D sensor data and evaluated in terms of its ability to estimate the current state of the object shape, in addition to predicting future states with the goal to plan and support the manipulation actions of a robotic hand.

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Section: Introductionsupporting

confidence: 81%

Combining Self-Organizing and Graph Neural Networks for Modeling Deformable Objects in Robotic Manipulation

Valencia

Payeur

2020

Front. Robot. AI

Self Cite

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show abstract

“…Third, we test the combination of the shape estimation and prediction methods as a learned model of deformable objects in real robotic environments. This paper represents a significant extension to previous work (Valencia et al, 2019) that corroborates the learned model across different types of deformable objects with experimental evaluations.…”

Section: Introductionsupporting

confidence: 69%

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“…In this section, we use the velocity command (20) to control the robot to deform the elastic rod into the pre-determined desired constant configuration c * , corresponding to s * . Also, we give the error criterion (26) to assess the deformation performance. Fig.…”

Section: Manipulation Of Elastic Rodsmentioning

confidence: 99%

“…In [29], a coarse-to-fine shape representation is proposed based on spatial transformer networks, which allows it to obtain good generalization properties without expensive ground truth observations. Growing neural gas was used in [26] to represent deformable shapes. In [18] a feature extractor based on the a convolutional autoencoder was proposed to obtain a low-dimensional latent space from tactile sensing data.…”

Section: Introductionmentioning

confidence: 99%

Towards Latent Space Based Manipulation of Elastic Rods using Autoencoder Models and Robust Centerline Extractions

Qi¹,

Ma²,

Zhou³

et al. 2021

Preprint

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The automatic shape control of deformable objects is an open (and currently hot) manipulation problem that is challenging due to the object's high-dimensional shape information and its complex physical properties. As a feasible solution to these issues, in this paper, we propose a new methodology to automatically deform elastic rods into 2D desired shapes. For that, we present an efficient vision-based controller that uses a deep autoencoder network to compute a compact representation of the object's infinite dimensional shape. To deal with the (typically unknown) mechanical properties of the object, we use an online algorithm that approximates the sensorimotor mapping between the robot's configuration and the object's shape features. Our new approach has the capability to compute the rod's centerline from raw visual data in real-time; This is done by introducing an adaptive algorithm based on a self-organizing network. The effectiveness of the proposed method is thoroughly validated with simulations and experiments.

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Toward Real-Time 3D Shape Tracking of Deformable Objects for Robotic Manipulation and Shape Control

Cited by 7 publications

References 13 publications

Combining Self-Organizing and Graph Neural Networks for Modeling Deformable Objects in Robotic Manipulation

Combining Self-Organizing and Graph Neural Networks for Modeling Deformable Objects in Robotic Manipulation

Data_Sheet_1.PDF

Towards Latent Space Based Manipulation of Elastic Rods using Autoencoder Models and Robust Centerline Extractions

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