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Hierarchical Tactile-Based Control Decomposition of Dexterous In-Hand Manipulation Tasks

Veiga, Filipe ; Akrour, Riad ; Peters, Jan (2020)
Hierarchical Tactile-Based Control Decomposition of Dexterous In-Hand Manipulation Tasks.
In: Frontiers in Robotics and AI, 7
doi: 10.3389/frobt.2020.521448
Artikel, Bibliographie

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Kurzbeschreibung (Abstract)

In-hand manipulation and grasp adjustment with dexterous robotic hands is a complex problem that not only requires highly coordinated finger movements but also deals with interaction variability. The control problem becomes even more complex when introducing tactile information into the feedback loop. Traditional approaches do not consider tactile feedback and attempt to solve the problem either by relying on complex models that are not always readily available or by constraining the problem in order to make it more tractable. In this paper, we propose a hierarchical control approach where a higher level policy is learned through reinforcement learning, while low level controllers ensure grip stability throughout the manipulation action. The low level controllers are independent grip stabilization controllers based on tactile feedback. The independent controllers allow reinforcement learning approaches to explore the manipulation tasks state-action space in a more structured manner. We show that this structure allows learning the unconstrained task with RL methods that cannot learn it in a non-hierarchical setting. The low level controllers also provide an abstraction to the tactile sensors input, allowing transfer to real robot platforms. We show preliminary results of the transfer of policies trained in simulation to the real robot hand.

Typ des Eintrags: Artikel
Erschienen: 2020
Autor(en): Veiga, Filipe ; Akrour, Riad ; Peters, Jan
Art des Eintrags: Bibliographie
Titel: Hierarchical Tactile-Based Control Decomposition of Dexterous In-Hand Manipulation Tasks
Sprache: Englisch
Publikationsjahr: 19 November 2020
Ort: Lausanne
Verlag: Frontiers Media S.A.
Titel der Zeitschrift, Zeitung oder Schriftenreihe: Frontiers in Robotics and AI
Jahrgang/Volume einer Zeitschrift: 7
Kollation: 12 Seiten
DOI: 10.3389/frobt.2020.521448
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Kurzbeschreibung (Abstract):

In-hand manipulation and grasp adjustment with dexterous robotic hands is a complex problem that not only requires highly coordinated finger movements but also deals with interaction variability. The control problem becomes even more complex when introducing tactile information into the feedback loop. Traditional approaches do not consider tactile feedback and attempt to solve the problem either by relying on complex models that are not always readily available or by constraining the problem in order to make it more tractable. In this paper, we propose a hierarchical control approach where a higher level policy is learned through reinforcement learning, while low level controllers ensure grip stability throughout the manipulation action. The low level controllers are independent grip stabilization controllers based on tactile feedback. The independent controllers allow reinforcement learning approaches to explore the manipulation tasks state-action space in a more structured manner. We show that this structure allows learning the unconstrained task with RL methods that cannot learn it in a non-hierarchical setting. The low level controllers also provide an abstraction to the tactile sensors input, allowing transfer to real robot platforms. We show preliminary results of the transfer of policies trained in simulation to the real robot hand.

Freie Schlagworte: tactile sensation and sensors, robotics, in-hand manipulation, hierarchical control, reinforcement learning
ID-Nummer: Artikel-ID: 521448
Zusätzliche Informationen:

Specialty section: This article was submitted to Sensor Fusion and Machine Perception, a section of the journal Frontiers in Robotics and AI

Sachgruppe der Dewey Dezimalklassifikatin (DDC): 000 Allgemeines, Informatik, Informationswissenschaft > 004 Informatik
Fachbereich(e)/-gebiet(e): 20 Fachbereich Informatik
20 Fachbereich Informatik > Intelligente Autonome Systeme
Hinterlegungsdatum: 12 Mär 2024 07:36
Letzte Änderung: 12 Mär 2024 07:36
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