Interlinked Visual Tracking and Robotic Manipulation of Articulated Objects

Paolillo, Antonio; Chappellet, Kévin; Bolotnikova, Anastasia; Kheddar, Abderrahmane

doi:10.1109/lra.2018.2835515

Cited by 24 publications

(13 citation statements)

References 22 publications

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“…In this paper, we integrated and enhanced different bricks to build an architecture that enables the humanoid robot Pepper to reach a person in need for physical assistance, communicate its intentions and establish several physical contacts to initiate the assistance process. We developed a taskspace optimization controller instance derived from [20], enhanced with visual servoing [22], which allows reaching safely a person. In close contact interaction the robot initiates contacts [7] to achieve compliant contact motion [8].…”

Section: Discussionmentioning

confidence: 99%

Autonomous Initiation of Human Physical Assistance by a Humanoid

Bolotnikova

Courtois

Kheddar

2020

2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)

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We study the use of humanoid robot technology for physical assistance in motion for a frail person. A careful design of a whole-body controller for a humanoid robot needs to be developed in order to ensure efficient, intuitive and secure interaction between humanoid-assistant and human-patient.Here, we present a design and implementation of a wholebody controller that enables a humanoid robot with a mobile base to autonomously reach a person, perform audiovisual communication of intent, and establish several physical contacts for initiating physical assistance. Our controller uses (i) visual human perception as a feedback for navigation and (ii) joint residual signal based contact detection for closed-loop physical contact creation. We assess the developed controller on a healthy subject and report on the experiments achieved and the results.

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Section: Discussionmentioning

confidence: 99%

Autonomous Initiation of Human Physical Assistance by a Humanoid

Bolotnikova

Courtois

Kheddar

2020

2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)

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“…A QP-based controller at velocity level includes the CM minimization to perform physio-therapeutic juggling with a humanoid in [14]. Concerning VS [9], [15], it is worth mentioning that it can be integrated in the optimization-based whole-body motion control of humanoids, as done within a QP in [16], and in [17] for estimating the configuration of an object to be manipulated. In [18], VS is integrated in a differential dynamic programming framework, which takes into account also the robot centroidal dynamics.…”

Section: State Of the Artmentioning

confidence: 99%

Exploiting visual servoing and centroidal momentum for whole-body motion control of humanoid robots in absence of contacts and gravity

Hoffman

Paolillo

2021

2021 IEEE International Conference on Robotics and Automation (ICRA)

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The big potential of humanoid robots is not restricted to the ground, but these versatile machines can be successfully employed in unconventional scenarios, e.g. space, where contacts are not always present. In these situations, the robot's limbs can be used to assist or even generate the angular motion of the floating base, as a consequence of the centroidal momentum conservation. In this paper, we propose to combine, in the same whole-body motion control, visual servoing and centroidal momentum conservation. The former dictates a rotation to the floating humanoid to achieve a task in the Cartesian space; the latter is exploited to realize the desired rotation by moving the robot's articulations. Simulations in a space scenario are carried out using COMAN, a humanoid robot developed at the Istituto Italiano di Tecnologia.

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“…Such objects inherently have more degrees of freedom (beyond the 6DoF pose associated to rigid objects), presenting an added challenge for robot perception and manipulation tasks. Many algorithms for the perception and manipulation of articulated objects directly or indirectly utilize kinematic models [30], [31], [32], so the unsupervised learning of kinematic models for articulated objects is a subject of great interest in robotics.…”

Section: Learning Kinematic Modelsmentioning

confidence: 99%

Topologically-Informed Atlas Learning

Cohn¹,

Devraj²,

Jenkins³

2021

Preprint

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We present a new technique that enables manifold learning to accurately embed data manifolds that contain holes, without discarding any topological information. Manifold learning aims to embed high dimensional data into a lower dimensional Euclidean space by learning a coordinate chart, but it requires that the entire manifold can be embedded in a single chart. This is impossible for manifolds with holes. In such cases, it is necessary to learn an atlas: a collection of charts that collectively cover the entire manifold. We begin with many small charts, and combine them in a bottom-up approach, where charts are only combined if doing so will not introduce problematic topological features. When it is no longer possible to combine any charts, each chart is individually embedded with standard manifold learning techniques, completing the construction of the atlas. We show the efficacy of our method by constructing atlases for challenging synthetic manifolds; learning human motion embeddings from motion capture data; and learning kinematic models of articulated objects.

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Interlinked Visual Tracking and Robotic Manipulation of Articulated Objects

Cited by 24 publications

References 22 publications

Autonomous Initiation of Human Physical Assistance by a Humanoid

Autonomous Initiation of Human Physical Assistance by a Humanoid

Exploiting visual servoing and centroidal momentum for whole-body motion control of humanoid robots in absence of contacts and gravity

Topologically-Informed Atlas Learning

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