An Incremental Learning Model for Mobile Robot: From Short-Term Memory to Long-Term Memory

Abstract

During the environmental cognition, how to realize the efficient incremental learning of a mobile robot is a great challenge. The existing methods suffer from the low efficiency. This article proposes a novel methodology to address it. A memory model inspired by the human brains is constructed to realize the transmission of short-term memory (STM) to long-term memory (LTM) in the offline states, thus realize the efficient incremental learning of the mobile robot. Concretely, during the online process, when the sensory information is input to the developmental network (DN), similarity between the new input information and the knowledge memorized in the DN is calculated first. If the similarity is larger than the threshold, this input is transmitted to motor layer and determine the optimal decision. Otherwise, the sensory input will be temporarily stored in a neuron in the STM by an evaluation function. During the offline states, a self-triggering mechanism is designed to trigger the DN to work again. Then, the lateral excitation of the internal neurons is designed to fire more neurons to memorize the knowledge transferred from the STM. Then, the synaptic weights of the new fire neurons are updated, and the STM becomes the LTM, thus realizing the incremental learning. In the following task, if the robot encounters a similar scenario, it can make a quick decision, based on the knowledge learned during the offline states. This continuous learning pattern reduces the training samples used in the network, hence enhancing its learning efficiency. Most importantly, this methodology makes the robot continuously improve its intelligence through the incremental learning, even in off-line states. Extensive simulation and experiment results of the mobile robot navigation demonstrate its potential.