Two people walking towards each other in a colliding course is an everyday problem of human-human interaction. In spite of the different environmental and individual factors that might jeopardise successful human trajectories, people are generally skilled at avoiding crashing into each other. However, it is not clear if the same strategies will apply when a human is in a colliding course with a robot, nor which (if any) robot-related factors will influence the human's decision to swerve or not. In this work, we present the results of an online study where participants walked towards a virtual robot that differed in terms of anthropomorphism and perceived autonomy, and had to decide whether to swerve, or continue straight. The experiment was inspired by the game-theoretic game of chicken. We found that people performed more swerving actions when they believed the robot to be teleoperated by another participant. When they swerved, they also swerved closer to the robot with high levels of human-likeness, and farther away from the robot with low anthropomorphism score, suggesting a higher uncertainty about the mechanical-looking robot's intentions. These results are discussed in the context of socially-aware robot navigation, and will be used to design novel algorithms for robot trajectories that take robot-related differences into account.

In this paper, we are interested in formalizing human trajectories in human-robot encounters. We consider a particular case where a human and a robot walk towards each other. A question that arises is whether, when, and how humans will deviate from their trajectory to avoid a collision. These human trajectories can then be used to generate socially acceptable robot trajectories. To model these trajectories, we propose a data-driven algorithm to extract a formal specification expressed in Signal Temporal Logic with probabilistic predicates. We evaluated our method on trajectories collected through an online study where participants had to avoid colliding with a robot in a shared environment. Further, we demonstrate that probabilistic STL is a suitable formalism to depict human behavior, choices and preferences in specific scenarios of social navigation.