A Method to Deal with Prospective Risks at Home in Robotic Observations by Using a Brain-Inspired Model

Abstract

Home robotics is a continuously growing field in academic research as well as commercial market. People are becoming more interested in advanced intelligent robots that can do housework and take care of children and elderly. A brain-inspired intelligent system is a possible solution to make the robot capable of learning and predicting risks at home. In order to solve difficult problems such as ambiguous situations and unclear causality, we propose a robotic system inspired from human working memory functions, which consists of an Event Map for storing observed information, and a Causality Map for representing causal relationships through supervised learning. The two maps couple together to enable the robot to evaluate various situations based on the appropriate context. More importantly, the Causality Map takes into account the dynamical aspects of physical attributes (e.g. the decreasing temperature of a hot pot). Our case studies showed that this is a satisfactory solution for predicting many risky situations at home.