Deep ART Neural Model for Biologically Inspired Episodic Memory and Its Application to Task Performance of Robots

• Published in: IEEE transactions on cybernetics Vol. 48; no. 6; pp. 1786 - 1799
• Main Authors: Park, Gyeong-Moon, Yoo, Yong-Ho, Kim, Deok-Hwa, Kim, Jong-Hwan
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.06.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Biological information theory; Biological system modeling; Deep adaptive resonance theory (ART); Encoding; episode learning; episode retrieval; Garbage; Humanoid; Humanoid robots; Intelligence; Robots; Robustness; Subspace constraints; Toys; Waste disposal; wheel-based humanoid robot
• ISSN: 2168-2267; 2168-2275
• DOI: 10.1109/TCYB.2017.2715338

Robots are expected to perform smart services and to undertake various troublesome or difficult tasks in the place of humans. Since these human-scale tasks consist of a temporal sequence of events, robots need episodic memory to store and retrieve the sequences to perform the tasks autonomously in similar situations. As episodic memory, in this paper we propose a novel Deep adaptive resonance theory (ART) neural model and apply it to the task performance of the humanoid robot, Mybot, developed in the Robot Intelligence Technology Laboratory at KAIST. Deep ART has a deep structure to learn events, episodes, and even more like daily episodes. Moreover, it can retrieve the correct episode from partial input cues robustly. To demonstrate the effectiveness and applicability of the proposed Deep ART, experiments are conducted with the humanoid robot, Mybot, for performing the three tasks of arranging toys, making cereal, and disposing of garbage.