ART neural network-based integration of episodic memory and semantic memory for task planning for robots

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Automated task planning for robots faces great challenges in that the sequences of events needed for a particular task are mostly required to be hard-coded. This can be a cumbersome process, especially, when the user wants a robot to learn a large number of similar tasks with different objects that are semantically related. We propose a novel approach of user preference-based integrated multi-memory model (pMM-ART). This approach focuses on exploiting a semantic hierarchy of objects alongside an episodic memory for enhancing the behavior of an autonomous agent. We analyze the functioning principle of the proposed model by teaching it a few distinct domestic tasks and observe that it is able to carry out a large number of similar tasks based on the semantic similarities between learned objects. We also demonstrate, via experiments using Mybot, our ability to reach those goals that are not possible without the integration of semantic knowledge with episodic memory.
Publisher
SPRINGER
Issue Date
2019-12
Language
English
Article Type
Article
Citation

AUTONOMOUS ROBOTS, v.43, no.8, pp.2163 - 2182

ISSN
0929-5593
DOI
10.1007/s10514-019-09868-x
URI
http://hdl.handle.net/10203/268017
Appears in Collection
EE-Journal Papers(저널논문)
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