A common approach to social distancing in robot navigation are spatial cost functions around humans that cause the robot to prefer paths that do not come too close to humans. However, in unpredictably dynamic scenarios, following such paths may produce robot behavior that appears confused. The concept of directional costs in cost functions [9] is supposed to alleviate this problem without incurring the problem of combinatorial explosions using temporal planning. With directional cost functions, a robot attempts to solve spatial conflicts by adjusting the velocity instead of the path, where possible. To complement results from simulations, in this paper we describe a user study we conducted with a PR2 robot and human participants to evaluate the new cost function type. The study shows that the real robot behavior is similar to the observations in simulation, and that participants rate the robot behavior less confusing with the adapted cost model. The study also shows other important behavior cues that can influence motion legibility.

Robot path planning has traditionally concentrated on collision-free paths. For robots that collaborate closely with humans, however, the situation is different in two respects: 1) the humans in the robot's environment are not randomly moving objects, but cognitive beings who can deliberately make way for a robot to pass and 2) the quality of a navigation plan depends less on quantitative efficiency criteria, but rather on the acceptance of humans. In this paper, we introduce a robot navigation approach that takes into account human-centered requirements and the collaborative nature of the interaction between the human and the robot.