Ploeger, Kai ; Lutter, Michael ; Peters, Jan (2022)
High Acceleration Reinforcement Learning for Real-World Juggling with Binary Rewards.
Conference on Robot Learning (CoRL) 2020. Cambridge MA, USA (16.-18.11.2020)
Konferenzveröffentlichung, Bibliographie
Dies ist die neueste Version dieses Eintrags.
Kurzbeschreibung (Abstract)
Robots that can learn in the physical world will be important to enable robots to escape their stiff and pre-programmed movements. For dynamic high-acceleration tasks, such as juggling, learning in the real-world is particularly challenging as one must push the limits of the robot and its actuation without harming the system, amplifying the necessity of sample efficiency and safety for robot learning algorithms. In contrast to prior work which mainly focuses on the learning algorithm, we propose a learning system, that directly incorporates these requirements in the design of the policy representation, initialization, and optimization. We demonstrate that this system enables the high-speed Barrett WAM manipulator to learn juggling two balls from 56 minutes of experience with a binary reward signal and finally juggles continuously for up to 33 minutes or about 4500 repeated catches. The videos documenting the learning process and the evaluation can be found at https://sites.google.com/view/jugglingbot
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2022 |
| Autor(en): | Ploeger, Kai ; Lutter, Michael ; Peters, Jan |
| Art des Eintrags: | Bibliographie |
| Titel: | High Acceleration Reinforcement Learning for Real-World Juggling with Binary Rewards |
| Sprache: | Englisch |
| Publikationsjahr: | 2022 |
| Ort: | Darmstadt |
| Verlag: | PMLR |
| Buchtitel: | Proceedings of the 2020 Conference on Robot Learning |
| Reihe: | Proceedings of Machine Learning Research |
| Band einer Reihe: | 155 |
| Kollation: | 12 Seiten |
| Veranstaltungstitel: | Conference on Robot Learning (CoRL) 2020 |
| Veranstaltungsort: | Cambridge MA, USA |
| Veranstaltungsdatum: | 16.-18.11.2020 |
| Zugehörige Links: | |
| Kurzbeschreibung (Abstract): | Robots that can learn in the physical world will be important to enable robots to escape their stiff and pre-programmed movements. For dynamic high-acceleration tasks, such as juggling, learning in the real-world is particularly challenging as one must push the limits of the robot and its actuation without harming the system, amplifying the necessity of sample efficiency and safety for robot learning algorithms. In contrast to prior work which mainly focuses on the learning algorithm, we propose a learning system, that directly incorporates these requirements in the design of the policy representation, initialization, and optimization. We demonstrate that this system enables the high-speed Barrett WAM manipulator to learn juggling two balls from 56 minutes of experience with a binary reward signal and finally juggles continuously for up to 33 minutes or about 4500 repeated catches. The videos documenting the learning process and the evaluation can be found at https://sites.google.com/view/jugglingbot |
| Freie Schlagworte: | Reinforcement Learning, Dynamic Manipulation, Juggling |
| Sachgruppe der Dewey Dezimalklassifikatin (DDC): | 000 Allgemeines, Informatik, Informationswissenschaft > 004 Informatik |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Intelligente Autonome Systeme |
| TU-Projekte: | EC/H2020|640554|SKILLS4ROBOTS |
| Hinterlegungsdatum: | 02 Aug 2024 12:45 |
| Letzte Änderung: | 02 Aug 2024 12:45 |
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| Suche nach Titel in: | TUfind oder in Google |
Verfügbare Versionen dieses Eintrags
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High Acceleration Reinforcement Learning for Real-World Juggling with Binary Rewards. (deposited 18 Nov 2022 14:40)
- High Acceleration Reinforcement Learning for Real-World Juggling with Binary Rewards. (deposited 02 Aug 2024 12:45) [Gegenwärtig angezeigt]
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