Pitz, Johannes and Röstel, Lennart and Sievers, Leon and Bäuml, Berthold (2023) Dextrous Tactile In-Hand Manipulation Using a Modular Reinforcement Learning Architecture. In: 2023 IEEE International Conference on Robotics and Automation, ICRA 2023, pp. 1852-1858. IEEE. International Conference on Robotics and Automation, 29 May - 3 Jun 2023, London, UK. doi: 10.1109/ICRA48891.2023.10160756. ISBN 979-835032365-8. ISSN 1050-4729.
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Official URL: https://ieeexplore.ieee.org/document/10160756
Abstract
Dextrous in-hand manipulation with a multi-fingered robotic hand is a challenging task, esp. when performed with the hand oriented upside down, demanding permanent force-closure, and when no external sensors are used. For the task of reorienting an object to a given goal orientation (vs. infinitely spinning it around an axis), the lack of external sensors is an additional fundamental challenge as the state of the object has to be estimated all the time, e.g., to detect when the goal is reached. In this paper, we show that the task of reorienting a cube to any of the 24 possible goal orientations in a Pi/2-raster using the torque-controlled DLR-Hand II is possible. The task is learned in simulation using a modular deep reinforcement learning architecture: the actual policy has only a small observation time window of 0.5s but gets the cube state as an explicit input which is estimated via a deep differentiable particle filter trained on data generated by running the policy. In simulation, we reach a success rate of 92% while applying significant domain randomization. Via zero-shot Sim2Real-transfer on the real robotic system, all 24 goal orientations can be reached with a high success rate.
Item URL in elib: | https://elib.dlr.de/195400/ | ||||||||||||||||||||
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Document Type: | Conference or Workshop Item (Poster) | ||||||||||||||||||||
Additional Information: | https://arxiv.org/abs/2303.04705 | ||||||||||||||||||||
Title: | Dextrous Tactile In-Hand Manipulation Using a Modular Reinforcement Learning Architecture | ||||||||||||||||||||
Authors: |
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Date: | June 2023 | ||||||||||||||||||||
Journal or Publication Title: | 2023 IEEE International Conference on Robotics and Automation, ICRA 2023 | ||||||||||||||||||||
Refereed publication: | Yes | ||||||||||||||||||||
Open Access: | No | ||||||||||||||||||||
Gold Open Access: | No | ||||||||||||||||||||
In SCOPUS: | Yes | ||||||||||||||||||||
In ISI Web of Science: | No | ||||||||||||||||||||
DOI: | 10.1109/ICRA48891.2023.10160756 | ||||||||||||||||||||
Page Range: | pp. 1852-1858 | ||||||||||||||||||||
Publisher: | IEEE | ||||||||||||||||||||
ISSN: | 1050-4729 | ||||||||||||||||||||
ISBN: | 979-835032365-8 | ||||||||||||||||||||
Status: | Published | ||||||||||||||||||||
Keywords: | In-Hand Manipulation, Deep Reinforcement Learning | ||||||||||||||||||||
Event Title: | International Conference on Robotics and Automation | ||||||||||||||||||||
Event Location: | London, UK | ||||||||||||||||||||
Event Type: | international Conference | ||||||||||||||||||||
Event Dates: | 29 May - 3 Jun 2023 | ||||||||||||||||||||
Organizer: | IEEE | ||||||||||||||||||||
HGF - Research field: | Aeronautics, Space and Transport | ||||||||||||||||||||
HGF - Program: | Space | ||||||||||||||||||||
HGF - Program Themes: | Robotics | ||||||||||||||||||||
DLR - Research area: | Raumfahrt | ||||||||||||||||||||
DLR - Program: | R RO - Robotics | ||||||||||||||||||||
DLR - Research theme (Project): | R - Autonomous learning robots [RO] | ||||||||||||||||||||
Location: | Oberpfaffenhofen | ||||||||||||||||||||
Institutes and Institutions: | Institute of Robotics and Mechatronics (since 2013) | ||||||||||||||||||||
Deposited By: | Pitz, Johannes | ||||||||||||||||||||
Deposited On: | 21 Sep 2023 10:32 | ||||||||||||||||||||
Last Modified: | 21 Sep 2023 10:32 |
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