Tracking elastic deformable objects with an RGB-D sensor for a pizza chef robot

Petit, Antoine; Lippiello, Vincenzo; Fontanelli, Giuseppe Andrea; Siciliano, Bruno

doi:10.1016/j.robot.2016.08.023

Cited by 50 publications

(41 citation statements)

References 14 publications

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“…A requirement for a reliable physics-based manipulation is the estimation of both E and ν. We employ the approach proposed in [23], which consists in minimizing a fitting error between the simulated deformations and the deformations captured by an RGB-D sensor (Asus Xtion). The simulation relies on the input force measured by means of a force sensor mounted on a robotic arm and on a simple interaction model between the object, the robotic tool and the underlying flat surface.…”

Section: A Estimation Of the Elasticity Parametersmentioning

confidence: 99%

“…Thus, adapting this framework to our active deformation control task with a robotic anthropomorphic hand appears as an appealing solution. Our strategy also takes advantage of the availability of a vision-based offline calibration technique for the estimation of the elasticity parameters [23]. As a preliminary work, we restrict the approach to an open-loop control scheme.…”

Section: Introductionmentioning

confidence: 99%

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FEM-Based Deformation Control for Dexterous Manipulation of 3D Soft Objects

Ficuciello¹,

Migliozzi²,

Coevoet³

et al. 2018

2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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In this paper, a method for dexterous manipulation of 3D soft objects for real-time deformation control is presented, relying on Finite Element modelling. The goal is to generate proper forces on the fingertips of an anthropomorphic device during in-hand manipulation to produce desired displacements of selected control points on the object. The desired motions of the fingers are computed in real-time as an inverse solution of a Finite Element Method (FEM), the forces applied by the fingertips at the contact points being modelled by Lagrange multipliers. The elasticity parameters of the model are preliminarly estimated using a vision system and a force sensor. Experimental results are shown with an underactuated anthropomorphic hand that performs a manipulation task on a soft cylindrical object.

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Section: A Estimation Of the Elasticity Parametersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

FEM-Based Deformation Control for Dexterous Manipulation of 3D Soft Objects

Ficuciello¹,

Migliozzi²,

Coevoet³

et al. 2018

2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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“…Using the point cloud data provided by the RGB-D sensor, a method to cope with real-time (35 fps) tracking of a deformable object is presented. Several contributions are proposed such as handling various large elastic deformations while ensuring physical consistency, coping with fractures, rigid motions and occlusions [17]. The case of a pizza dough being stretched and tossed is taken as example.…”

Section: Perception Of Deformable Objectsmentioning

confidence: 99%

“…An Euler implicit integration scheme and a conjugate gradient method are used to solve the system with respect x. The elastic forces f ext based on geometrical correspondences between the point cloud and the mesh [17].…”

Section: Perception Of Deformable Objectsmentioning

confidence: 99%

Nonprehensile Manipulation of Deformable Objects: Achievements and Perspectives from the Robotic Dynamic Manipulation Project

Ruggiero

Petit²,

Serra

et al. 2018

IEEE Robot. Automat. Mag.

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The goal of this work is to disseminate the results achieved so far within the RODYMAN project related to planning and control strategies for robotic nonprehensile manipulation. The project aims at advancing the state of the art of nonprehensile dynamic manipulation of rigid and deformable objects to future enhance the possibility of employing robots in anthropic environments. The final demonstrator of the RODYMAN project will be an autonomous pizza maker. This article is a milestone to highlight the lessons learned so far and pave the way towards future research directions and critical discussions.

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“…However, most of the current industrial robots are not able to control the deformation of an object because it requires both knowledge of the object physical properties and real-time tracking of the deformations. Several studies have focused on real-time deformation tracking [4]- [7] but not simultaneously with deformation control. A few methods have been proposed to find a grasp configuration on a deformable object [8] but without interactive control of the deformation.…”

Section: Introductionmentioning

confidence: 99%

Active Deformation through Visual Servoing of Soft Objects

Lagneau

Krupa

Marchal

2020

2020 IEEE International Conference on Robotics and Automation (ICRA)

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In this paper, we propose the ADVISEd (Active Deformation through VIsual SErvoing) method, a novel modelfree deformation servoing method able to deform a soft object towards a desired shape. ADVISEd relies on an online estimation of the deformation Jacobian that relates the motion of the robot end-effector to the deformation behavior of the object. The estimation is based on a weighted least-squares minimization with a sliding window. The robustness of the method to observation noise is ensured using an eigenvaluebased confidence criterion. The ADVISEd method is validated through comparisons with a model-based and a model-free state-of-the-art methods. Two experimental setups are proposed to compare the methods, one to perform a marker-based active shaping task and one to perform several marker-less active shaping and shape preservation tasks. Experiments showed that our approach can interactively control the deformations of an object in different tasks while ensuring better robustness to external perturbations than the state-of-the-art methods.

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Tracking elastic deformable objects with an RGB-D sensor for a pizza chef robot

Cited by 50 publications

References 14 publications

FEM-Based Deformation Control for Dexterous Manipulation of 3D Soft Objects

FEM-Based Deformation Control for Dexterous Manipulation of 3D Soft Objects

Nonprehensile Manipulation of Deformable Objects: Achievements and Perspectives from the Robotic Dynamic Manipulation Project

Active Deformation through Visual Servoing of Soft Objects

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