SR-EM: Episodic Memory Aware of Semantic Relations Based on Hierarchical Clustering Resonance Network.

An intelligent robot requires episodic memory that can retrieve a sequence of events for a service task learned from past experiences to provide a proper service to a user. Various episodic memories, which can learn new tasks incrementally without forgetting the tasks learned previously, have been designed based on adaptive resonance theory (ART) networks. The conventional ART-based episodic memories, however, do not have the adaptability to the changing environments.

Adaptive Developmental Resonance Network.

Adaptive resonance theory (ART) networks, including developmental resonance network (DRN), basically use a vigilance parameter as a hyperparameter to determine whether a current input can belong to any existing categories or not. The problem here is that the clustering quality of those networks is sensitive to the vigilance parameter so that the users are required to fine-tune the parameter delicately beforehand. Another problem is that those networks only deal with a hyperrectangular decision boundary, which means they cannot learn categories of arbitrary shape.

Convolutional Neural Network With Developmental Memory for Continual Learning.

Convolutional neural networks (CNNs) are one of the most successful deep neural networks. Indeed, most of the recent applications related to computer vision are based on CNNs. However, when learning new tasks in a sequential manner, CNNs face catastrophic forgetting: they forget a considerable amount of previously learned tasks while adapting to novel tasks.

Developmental Resonance Network.

Adaptive resonance theory (ART) networks deal with normalized input data only, which means that they need the normalization process for the raw input data, under the assumption that the upper and lower bounds of the input data are known in advance. Without such an assumption, ART networks cannot be utilized. To solve this problem and improve the learning performance, inspired by the ART networks, we propose a developmental resonance network (DRN) by employing new techniques of a global weight and node connection and grouping processes.

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

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.