Deep ART Neural Model for Biologically Inspired Episodic Memory and Its Application to Task Performance of Robots
提出一种深度自适应共振理论（ART）神经网络模型，使机器人能像人类一样存储和回忆事件序列，并在类似情境中自主执行任务。在Mybot人形机器人上验证了整理玩具、做麦片和倒垃圾三项任务。
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.