  Mobile robots are traditionally developed to be reactive and avoid collisions
with surrounding humans, often moving in unnatural ways without following
social protocols, forcing people to behave very differently from human-human
interaction rules. Humans, on the other hand, are seamlessly able to understand
why they may interfere with surrounding humans and change their behavior based
on their reasoning, resulting in smooth, intuitive avoiding behaviors. In this
paper, we propose an approach for a mobile robot to avoid interfering with the
desired paths of surrounding humans. We leverage a library of previously
observed trajectories to design a decision-tree based interpretable monitor
that: i) predicts whether the robot is interfering with surrounding humans, ii)
explains what behaviors are causing either prediction, and iii) plans
corrective behaviors if interference is predicted. We also propose a validation
scheme to improve the predictive model at run-time. The proposed approach is
validated with simulations and experiments involving an unmanned ground vehicle
(UGV) performing go-to-goal operations in the presence of humans, demonstrating
non-interfering behaviors and run-time learning.

Robotics