Object-Goal Navigation of Home Care Robot Based on Human Activity Inference and Cognitive Memory

Abstract

As older adults' memory and cognitive ability deteriorate, designing a cognitive robot system to find the desired objects for users becomes more critical. Cognitive abilities, such as detecting and memorizing the environment and human activities are crucial in implementing effective human–robot interaction and navigation. In addition, robots must possess language understanding capabilities to comprehend human speech and respond promptly. This research aims to develop a mobile robot system for home care that incorporates human activity inference and cognitive memory to reason about the target object's location and navigate to find it. The method comprises three modules: 1) an object-goal navigation module for mapping the environment, detecting surrounding objects, and navigating to find the target object, 2) a cognitive memory module for recognizing human activity and storing encoded information, and 3) an interaction module to interact with humans and infer the target object's position. By leveraging Big Data, human cues, and a commonsense knowledge graph, the system can efficiently and robustly search for target objects. The effectiveness of the system is validated through both simulated and real-world scenarios.