Prediction of human collision avoidance behavior by lifelong learning for socially compliant robot navigation

In order to act socially compliant with humans, mobile robots need to show several behaviors that require the prediction of people's motion. For example, when a robot avoids a person, it needs to respect the human's personal space [1] and the avoidance behavior needs to be smooth, so that it is understandable to the interaction partner. To achieve this, the robot needs to reason about future paths a person is likely to follow. Because humans adapt their avoidance behavior to the robot's motion, the proposed method performs lifelong learning of the people's behavior while it adapts its own behavior to their motion. The human avoidance behavior is modeled by a discrete, multi-modal, spatio-temporal distribution over the people's future occurrences. This prediction is based on the people's positions and their velocities relatively to the robot and the obstacle situation of the robot's environment. The proposed prediction method is significantly better than a simple linear prediction. Particularly, for tactical decisions, like whether to avoid a moving person on the left or on the right side, this approach is well suited. Furthermore, when the humans get used to a robot, also a long-term change of the human behavior towards the robot can be learned by our approach.