Task Intelligence of Robots: Neural Model-Based Mechanism of Thought and Online Motion Planning

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The crux of the realization of task intelligence for robots is to design the memory module for storing temporal event sequences of tasks, the mechanism of thought for reasoning, and motion planning methodology for execution, among others. In this paper, task intelligence is realized using episodic memory, neural model-based mechanism of thought, and an online motion planning algorithm. Robots are taught either by demonstration or symbolic description. A behavior appropriate to the current situation is selected by the developmental episodic memory-based mechanism of thought, while a proper task is retrieved from Deep adaptive resonance theory (ART). The behaviors are executed safely and quickly with the proposed motion planning algorithm. The effectiveness and applicability of task intelligence are demonstrated through experiments with the humanoid robot, Mybot, developed in the Robot Intelligence Technology Laboratory at KAIST.
Publisher
IEEE
Issue Date
2017-02
Language
English
Citation

IEEE Transactions on Emerging Topics in Computational Intelligence, v.1, no.1, pp.41 - 50

ISSN
2471-285X
DOI
10.1109/TETCI.2016.2645720
URI
http://hdl.handle.net/10203/228478
Appears in Collection
EE-Journal Papers(저널논문)
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