On Legible and Predictable Robot Navigation in Multi-Agent Environments

Legibility has recently become an important property to consider in the design of social navigation planners. Legible motion is intent-expressive, which when employed during social robot navigation, allows others to quickly infer the intended avoidance strategy. Predictability, although less commonly studied for social navigation, is, in a sense, the dual notion of legibility, and should also be accounted for in order to promote efficient motions. Predictable motion matches an observer's expectation which, during navigation, allows others to confidently carryout the interaction. In this work, we present a navigation framework capable of reasoning on its legibility and predictability with respect to dynamic interactions, e.g., a passing side. Our approach generalizes the previously formalized notions of legibility and predictability by allowing dynamic goal regions in order to navigate in dynamic environments. This generalization also allows us to quantitatively evaluate the legibility and the predictability of trajectories with respect to navigation interactions. Our approach is shown to promote legible behavior in ambiguous scenarios and predictable behavior in unambiguous scenarios. We also provide an adaptation to the multi-agent case, allowing the robot to reason on its legibility and predictability with respect to multiple interactions simultaneously. This adaptation promotes behaviors that are not illegible to other agents in the environment. In simulation, this is shown to resolve scenarios of high-complexity in an efficient manner. Furthermore, our approach yields an increase in safety while remaining competitive in terms of goal-efficiency when compared to other robot navigation planners in randomly generated multi-agent environments

Differences of Human Perceptions of a Robot Moving using Linear or Slow in, Slow out Velocity Profiles When Performing a Cleaning Task

We investigated how a robot moving with different velocity profiles affects a person's perception of it when working together on a task. The two profiles are the standard linear profile and a profile based on the animation principles of slow in, slow out. The investigation was accomplished by running an experiment in a home context where people and the robot cooperated on a clean-up task. We used the Godspeed series of questionnaires to gather people's perception of the robot. Average scores for each series appear not to be different enough to reject the null hypotheses, but looking at the component items provides paths to future areas of research. We also discuss the scenario for the experiment and how it may be used for future research into using animation techniques for moving robots and improving the legibility of a robot's locomotion