Social Navigation with Human Empowerment driven Deep Reinforcement Learning
This addresses the need for socially-compliant robots in shared workspaces to improve human acceptance, representing an incremental advancement in social navigation.
The paper tackled the problem of enabling mobile robots to navigate in a socially-compliant manner by minimizing disturbance to humans, using a deep reinforcement learning approach with intrinsic motivation based on human empowerment. The result showed that the method reduced human travel time and was rated as more social than state-of-the-art approaches in a user study.
Mobile robot navigation has seen extensive research in the last decades. The aspect of collaboration with robots and humans sharing workspaces will become increasingly important in the future. Therefore, the next generation of mobile robots needs to be socially-compliant to be accepted by their human collaborators. However, a formal definition of compliance is not straightforward. On the other hand, empowerment has been used by artificial agents to learn complicated and generalized actions and also has been shown to be a good model for biological behaviors. In this paper, we go beyond the approach of classical \acf{RL} and provide our agent with intrinsic motivation using empowerment. In contrast to self-empowerment, a robot employing our approach strives for the empowerment of people in its environment, so they are not disturbed by the robot's presence and motion. In our experiments, we show that our approach has a positive influence on humans, as it minimizes its distance to humans and thus decreases human travel time while moving efficiently towards its own goal. An interactive user-study shows that our method is considered more social than other state-of-the-art approaches by the participants.