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.

中文翻译：

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生物启发情景记忆的深度 ART 神经模型及其在机器人任务表现中的应用


机器人有望代替人类执行智能服务并承担各种麻烦或困难的任务。由于这些人类规模的任务由事件的时间序列组成，因此机器人需要情景记忆来存储和检索序列，以便在类似情况下自主执行任务。作为情景记忆，在本文中，我们提出了一种新颖的深度自适应共振理论（ART）神经模型，并将其应用于韩国科学技术院机器人智能技术实验室开发的人形机器人 Mybot 的任务表现。 Deep ART 具有深层结构，可以学习事件、情节，甚至更像日常情节。此外，它可以从部分输入线索中稳健地检索正确的情节。为了证明所提出的 Deep ART 的有效性和适用性，我们用人形机器人 Mybot 进行了实验，用于执行排列玩具、制作麦片和处理垃圾这三项任务。