Recognition and Identification of Intentional Blocking in Social Navigation

One of the most studied interactions in social navigation is a collision between a human and a robot. An overwhelming majority of these studies focus on collision avoidance: shifting away from such situations or staying still until the conflict is resolved. However, to act socially, avoidance is not always the desired behavior. Consider a staff member in a hospital blocking a delivery robot's path to type in a new delivery request. The robot should not steer away but rather stay put or even get closer to the person. This research provides a novel perspective on obstructions in social navigation. It does so by providing a vocabulary to distinguish intentional obstructions from unintentional blockings, and by designing a general obstruction-handling solution that can be augmented into robots both in academia and industry. This solution is named NIMBLE: Navigational Intentions Model for BLocking Estimation, and it provides a pipeline for handling intentional obstructions that is general enough to allow for varying implementations while maintaining a clear inference process for intentional obstructions. NIMBLE is evaluated using a case study of a robot navigating in a hospital. The paper provides a statistical analysis based on generated data and an exploratory evaluation using inputs from the robot's sensors. Both effectively illustrate NIMBLE's ability to distinguish between various intention types accurately.