Title: | Hierarchical learning of robotic contact policies |
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Authors: | ID Simonič, Mihael, Institut Jožef Stefan (Author) ID Ude, Aleš, Institut Jožef Stefan (Author) ID Nemec, Bojan, Institut Jožef Stefan (Author) |
Files: | URL - Source URL, visit https://www.sciencedirect.com/science/article/pii/S0736584523001321
PDF - Presentation file, download (1,73 MB) MD5: E411C83073559D4F47042661E627F1FD
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Language: | English |
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Typology: | 1.01 - Original Scientific Article |
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Organization: | IJS - Jožef Stefan Institute
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Abstract: | The paper addresses the issue of learning tasks where a robot maintains permanent contact with the environment. We propose a new methodology based on a hierarchical learning scheme coupled with task representation through directed graphs. These graphs are constituted of nodes and branches that correspond to the states and robotic actions, respectively. The upper level of the hierarchy essentially operates as a decision-making algorithm. It leverages reinforcement learning (RL) techniques to facilitate optimal decision-making. The actions are generated by a constraint-space following (CSF) controller that autonomously identifies feasible directions for motion. The controller generates robot motion by adjusting its stiffness in the direction defined by the Frenet–Serret frame, which is aligned with the robot path. The proposed framework was experimentally verified through a series of challenging robotic tasks such as maze learning, door opening, learning to shift the manual car gear, and learning car license plate light assembly by disassembly. |
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Keywords: | autonomous robot learning, learning, experience, compliance and impedance contro |
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Publication status: | Published |
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Publication version: | Version of Record |
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Submitted for review: | 12.09.2022 |
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Article acceptance date: | 08.09.2023 |
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Publication date: | 18.09.2023 |
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Publisher: | Elsevier |
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Year of publishing: | 2023 |
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Number of pages: | [in press] |
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Numbering: | Vol. 86 |
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Source: | Nizozemska |
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PID: | 20.500.12556/DiRROS-17072 ![Link is opened in a new window New window](teme/dirros/img/NovoOkno.png) |
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UDC: | 004 |
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ISSN on article: | 1879-2537 |
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DOI: | doi.org/10.1016/j.rcim.2023.102657 ![Link is opened in a new window New window](teme/dirros/img/NovoOkno.png) |
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COBISS.SI-ID: | 164894211 ![Link is opened in a new window New window](teme/dirros/img/NovoOkno.png) |
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Copyright: | © 2023 The Authors. Published by Elsevier Ltd. |
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Note: | Nasl. z nasl. zaslona;
Opis vira z dne 19. 9. 2023;
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Publication date in DiRROS: | 21.09.2023 |
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Views: | 619 |
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Downloads: | 346 |
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Metadata: | ![Export as DC-RDF DC-RDF](teme/dirros/img/icon_dc_rdf.gif) |
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