Hierarchical Tactile-Based Control Decomposition of Dexterous In-Hand Manipulation Tasks
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frobt-07-521448.pdf
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2.85 MB
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Adobe PDF
Checksum (MD5)
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Author(s)
Fernandes Veiga, Filipe
Akrour, Riad
Peters, Jan
Date Issued
November 2020
Journal
Frontiers in Robotics and AI
Publisher
Frontiers Media SA
Citation
Veiga, Filipe et al. "Hierarchical Tactile-Based Control Decomposition of Dexterous In-Hand Manipulation Tasks." Frontiers in Robotics and AI 7 (November 2020): 521448 © 2020 Veiga et al.
Version
Final published version
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.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.3389/frobt.2020.521448
