Tanneberg, Daniel ; Peters, Jan ; Rueckert, Elmar (2022)
Online Learning with Stochastic Recurrent Neural Networks using Intrinsic Motivation Signals.
CoRL2017 - Conference on Robot Learning 2017. Mountain View, California (13.11.2017-15.11.2017)
doi: 10.26083/tuprints-00020580
Konferenzveröffentlichung, Zweitveröffentlichung, Verlagsversion
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Kurzbeschreibung (Abstract)
Continuous online adaptation is an essential ability for the vision of fully autonomous and lifelong-learning robots. Robots need to be able to adapt to changing environments and constraints while this adaption should be performed without interrupting the robot’s motion. In this paper, we introduce a framework for probabilistic online motion planning and learning based on a bio-inspired stochastic recurrent neural network. Furthermore, we show that the model can adapt online and sample-efficiently using intrinsic motivation signals and a mental replay strategy. This fast adaptation behavior allows the robot to learn from only a small number of physical interactions and is a promising feature for reusing the model in different environments. We evaluate the online planning with a realistic dynamic simulation of the KUKA LWR robotic arm. The efficient online adaptation is shown in simulation by learning an unknown workspace constraint using mental replay and cognitive dissonance as intrinsic motivation signal.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2022 |
| Autor(en): | Tanneberg, Daniel ; Peters, Jan ; Rueckert, Elmar |
| Art des Eintrags: | Zweitveröffentlichung |
| Titel: | Online Learning with Stochastic Recurrent Neural Networks using Intrinsic Motivation Signals |
| Sprache: | Englisch |
| Publikationsjahr: | 2022 |
| Ort: | Darmstadt |
| Publikationsdatum der Erstveröffentlichung: | 2022 |
| Verlag: | PMLR |
| Buchtitel: | Proceedings of the 1st Annual Conference on Robot Learning |
| Reihe: | Proceedings of Machine Learning Research |
| Band einer Reihe: | 78 |
| Kollation: | 8 Seiten |
| Veranstaltungstitel: | CoRL2017 - Conference on Robot Learning 2017 |
| Veranstaltungsort: | Mountain View, California |
| Veranstaltungsdatum: | 13.11.2017-15.11.2017 |
| DOI: | 10.26083/tuprints-00020580 |
| URL / URN: | https://tuprints.ulb.tu-darmstadt.de/20580 |
| Zugehörige Links: | |
| Herkunft: | Zweitveröffentlichungsservice |
| Kurzbeschreibung (Abstract): | Continuous online adaptation is an essential ability for the vision of fully autonomous and lifelong-learning robots. Robots need to be able to adapt to changing environments and constraints while this adaption should be performed without interrupting the robot’s motion. In this paper, we introduce a framework for probabilistic online motion planning and learning based on a bio-inspired stochastic recurrent neural network. Furthermore, we show that the model can adapt online and sample-efficiently using intrinsic motivation signals and a mental replay strategy. This fast adaptation behavior allows the robot to learn from only a small number of physical interactions and is a promising feature for reusing the model in different environments. We evaluate the online planning with a realistic dynamic simulation of the KUKA LWR robotic arm. The efficient online adaptation is shown in simulation by learning an unknown workspace constraint using mental replay and cognitive dissonance as intrinsic motivation signal. |
| Freie Schlagworte: | Lifelong-learning, Intrinsic Motivation, Recurrent Neural Networks |
| Status: | Verlagsversion |
| URN: | urn:nbn:de:tuda-tuprints-205803 |
| 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: | 18 Nov 2022 14:30 |
| Letzte Änderung: | 21 Nov 2022 10:43 |
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| Suche nach Titel in: | TUfind oder in Google |
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- Online Learning with Stochastic Recurrent Neural Networks using Intrinsic Motivation Signals. (deposited 18 Nov 2022 14:30) [Gegenwärtig angezeigt]
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