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Editorial: Robots that Learn and Reason: Towards Learning Logic Rules from Noisy Data

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
Article, Secondary publication, Publisher's Version

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Item Type: Article
Type of entry: Secondary publication
Title: Editorial: Robots that Learn and Reason: Towards Learning Logic Rules from Noisy Data
Language: English
Date: 19 January 2024
Place of Publication: Darmstadt
Year of primary publication: 2021
Place of primary publication: Lausanne
Publisher: Frontiers Media S.A.
Journal or Publication Title: Frontiers in Robotics and AI
Volume of the journal: 8
Collation: 2 Seiten
DOI: 10.26083/tuprints-00019983
Corresponding Links:
Origin: Secondary publication DeepGreen
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.

Uncontrolled Keywords: learning logic rules, robotics, predicate grounding, two-level planning, reinforcement learning
Status: Publisher's Version
URN: urn:nbn:de:tuda-tuprints-199838
Additional Information:

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

Classification DDC: 000 Generalities, computers, information > 004 Computer science
600 Technology, medicine, applied sciences > 600 Technology
Divisions: Zentrale Einrichtungen > Centre for Cognitive Science (CCS)
Date Deposited: 19 Jan 2024 14:15
Last Modified: 08 Mar 2024 07:43
SWORD Depositor: Deep Green
URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/19983
PPN: 51607525X
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