Koert, Dorothea ; Maeda, Guilherme ; Neumann, Gerhard ; Peters, Jan (2022)
Learning Coupled Forward-Inverse Models with Combined Prediction Errors.
International Conference on Robotics and Automation (ICRA) 2018. Brisbane, QLD, Australia (21.05.2018-25.05.2018)
doi: 10.26083/tuprints-00020546
Konferenzveröffentlichung, Zweitveröffentlichung, Postprint
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Kurzbeschreibung (Abstract)
Challenging tasks in unstructured environments require robots to learn complex models. Given a large amount of information, learning multiple simple models can offer an efficient alternative to a monolithic complex network. Training multiple models-that is, learning their parameters and their responsibilities-has been shown to be prohibitively hard as optimization is prone to local minima. To efficiently learn multiple models for different contexts, we thus develop a new algorithm based on expectation maximization (EM). In contrast to comparable concepts, this algorithm trains multiple modules of paired forward-inverse models by using the prediction errors of both forward and inverse models simultaneously. In particular, we show that our method yields a substantial improvement over only considering the errors of the forward models on tasks where the inverse space contains multiple solutions.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2022 |
| Autor(en): | Koert, Dorothea ; Maeda, Guilherme ; Neumann, Gerhard ; Peters, Jan |
| Art des Eintrags: | Zweitveröffentlichung |
| Titel: | Learning Coupled Forward-Inverse Models with Combined Prediction Errors |
| Sprache: | Englisch |
| Publikationsjahr: | 2022 |
| Ort: | Darmstadt |
| Publikationsdatum der Erstveröffentlichung: | 2022 |
| Verlag: | IEEE |
| Buchtitel: | 2018 IEEE International Conference on Robotics and Automation (ICRA) |
| Kollation: | 7 Seiten |
| Veranstaltungstitel: | International Conference on Robotics and Automation (ICRA) 2018 |
| Veranstaltungsort: | Brisbane, QLD, Australia |
| Veranstaltungsdatum: | 21.05.2018-25.05.2018 |
| DOI: | 10.26083/tuprints-00020546 |
| URL / URN: | https://tuprints.ulb.tu-darmstadt.de/20546 |
| Zugehörige Links: | |
| Herkunft: | Zweitveröffentlichungsservice |
| Kurzbeschreibung (Abstract): | Challenging tasks in unstructured environments require robots to learn complex models. Given a large amount of information, learning multiple simple models can offer an efficient alternative to a monolithic complex network. Training multiple models-that is, learning their parameters and their responsibilities-has been shown to be prohibitively hard as optimization is prone to local minima. To efficiently learn multiple models for different contexts, we thus develop a new algorithm based on expectation maximization (EM). In contrast to comparable concepts, this algorithm trains multiple modules of paired forward-inverse models by using the prediction errors of both forward and inverse models simultaneously. In particular, we show that our method yields a substantial improvement over only considering the errors of the forward models on tasks where the inverse space contains multiple solutions. |
| Status: | Postprint |
| URN: | urn:nbn:de:tuda-tuprints-205461 |
| 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 14:02 |
| Letzte Änderung: | 21 Nov 2022 10:47 |
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| Suche nach Titel in: | TUfind oder in Google |
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