Acquisition of Inducing Policy in Collaborative Robot Navigation Based on Multiagent Deep Reinforcement Learning

To avoid inefficient movement or the freezing problem in crowded environments, we previously proposed a human-aware interactive navigation method that uses inducement, i.e., voice reminders or physical touch. However, the use of inducement largely depends on many factors, including human attributes, task contents, and environmental contexts. Thus, it is unrealistic to pre-design a set of parameters such as the coefficients in the cost function, personal space, and velocity in accordance with the situation. To understand and evaluate if inducement (voice reminder in this study) is effective and how and when it must be used, we propose to comprehend them through multiagent deep reinforcement learning in which the robot voluntarily acquires an inducing policy suitable for the situation. Specifically, we evaluate whether a voice reminder can improve the time to reach the goal by learning when the robot uses it. Results of simulation experiments with four different situations show that the robot could learn inducing policies suited for each situation, and the effectiveness of inducement is greatly improved in more congested and narrow situations.