Moreno, Plinio ; Bernardino, Alexandre ; Santos-Victor, José ; Ventura, Rodrigo ; Kersting, Kristian (2024)
Editorial: Robots that Learn and Reason: Towards Learning Logic Rules from Noisy Data.
In: Frontiers in Robotics and AI, 2021, 8
doi: 10.26083/tuprints-00019983
Artikel, Zweitveröffentlichung, Verlagsversion
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Kurzbeschreibung (Abstract)
From the early developments of AI applied to robotics by Hart et al. (1968), Duda and Hart (1972) and Lozano-Pérez and Wesley (1979), higher level commands were grounded to real world sensing by carefully design algorithms, which provide a link between the abstract predicates and the sensors and actuators. In order to have fully autonomous robots that learn by exploration and by imitation, the grounding algorithms between the higher-level predicates and the lower-level sensors and actuators should be discovered by the robot. Previous and recent efforts on robotics aim to discover and/or learn these intermediate layer commands, which must cope with discrete and continuous data. The main objective of this Research Topic is to advance on learning logic rules from noisy data. We have four articles that address: Logic rules that cope with states that are not directly observable by the sensing modalities; learning rules that represent object properties and their functionalities, which are grounded to the particular robot experience; learning low-level robot control actions that fulfill a set of abstract predicates in a two-level planning approach; learning to develop skills in a robotic playing scenario by composing a set of behaviors. In the following, we introduce the four articles and their contributions to rule learning in presence of noisy data.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2024 |
| Autor(en): | Moreno, Plinio ; Bernardino, Alexandre ; Santos-Victor, José ; Ventura, Rodrigo ; Kersting, Kristian |
| Art des Eintrags: | Zweitveröffentlichung |
| Titel: | Editorial: Robots that Learn and Reason: Towards Learning Logic Rules from Noisy Data |
| Sprache: | Englisch |
| Publikationsjahr: | 19 Januar 2024 |
| Ort: | Darmstadt |
| Publikationsdatum der Erstveröffentlichung: | 2021 |
| Ort der Erstveröffentlichung: | Lausanne |
| Verlag: | Frontiers Media S.A. |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Frontiers in Robotics and AI |
| Jahrgang/Volume einer Zeitschrift: | 8 |
| Kollation: | 2 Seiten |
| DOI: | 10.26083/tuprints-00019983 |
| URL / URN: | https://tuprints.ulb.tu-darmstadt.de/19983 |
| Zugehörige Links: | |
| Herkunft: | Zweitveröffentlichung DeepGreen |
| Kurzbeschreibung (Abstract): | From the early developments of AI applied to robotics by Hart et al. (1968), Duda and Hart (1972) and Lozano-Pérez and Wesley (1979), higher level commands were grounded to real world sensing by carefully design algorithms, which provide a link between the abstract predicates and the sensors and actuators. In order to have fully autonomous robots that learn by exploration and by imitation, the grounding algorithms between the higher-level predicates and the lower-level sensors and actuators should be discovered by the robot. Previous and recent efforts on robotics aim to discover and/or learn these intermediate layer commands, which must cope with discrete and continuous data. The main objective of this Research Topic is to advance on learning logic rules from noisy data. We have four articles that address: Logic rules that cope with states that are not directly observable by the sensing modalities; learning rules that represent object properties and their functionalities, which are grounded to the particular robot experience; learning low-level robot control actions that fulfill a set of abstract predicates in a two-level planning approach; learning to develop skills in a robotic playing scenario by composing a set of behaviors. In the following, we introduce the four articles and their contributions to rule learning in presence of noisy data. |
| Freie Schlagworte: | learning logic rules, robotics, predicate grounding, two-level planning, reinforcement learning |
| Status: | Verlagsversion |
| URN: | urn:nbn:de:tuda-tuprints-199838 |
| Zusätzliche Informationen: | This article is part of the Research Topic Robots that Learn and Reason: Towards Learning Logic Rules from Noisy Data This article was submitted to Computational Intelligence in Robotics, a section of the journal Frontiers in Robotics and AI |
| Sachgruppe der Dewey Dezimalklassifikatin (DDC): | 000 Allgemeines, Informatik, Informationswissenschaft > 004 Informatik 600 Technik, Medizin, angewandte Wissenschaften > 600 Technik |
| Fachbereich(e)/-gebiet(e): | Zentrale Einrichtungen Zentrale Einrichtungen > Centre for Cognitive Science (CCS) |
| Hinterlegungsdatum: | 19 Jan 2024 14:15 |
| Letzte Änderung: | 25 Jan 2024 10:03 |
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| Suche nach Titel in: | TUfind oder in Google |
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- Editorial: Robots that Learn and Reason: Towards Learning Logic Rules from Noisy Data. (deposited 19 Jan 2024 14:15) [Gegenwärtig angezeigt]
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