A companion robot for daily care of elders based on homeostasis

This paper presents a hybrid system of applying homeostatic drives theory and stimuli from environment or human on a robot, aiming to solve the increasing need of elder caring in modern aging society. Through reinforcement learning with human feedback, the robot is enabled to make appropriate decisions by reviewing its own motivation, environment stimuli and human inputs, allowing the robot to finish vague tasks, i.e. to serve, help or have a conversation with human on different situations. In our experiment, the human satisfaction rate could reach 94% and the robot could as well maintain a stable condition to perform long-term service. If the robot is to be deployed in a nursing home, it can further serve and meet the requirements of elders, while simultaneously maintaining its own system, such as battery level, in regular daily lives.