In this paper, we revisit a real-time socially-aware navigation planner which helps a mobile robot to navigate alongside humans in a socially acceptable manner. This navigation planner is a modification of nav_core package of Robot Operating System (ROS) originally presented in, modified to use only egocentric sensors. The planner can be utilized to provide safe as well as socially appropriate robot navigation. Primitive features including interpersonal distance between the robot and an interaction partner, distances between these agents and features of the environment (such as hallways detected in real-time) are used to reason about the current state of an interaction. Gaussian Mixture Models (GMM) are trained over these features from human-human interaction demonstrations of various interaction scenarios. This model is both used to discriminate different human actions related to their navigation behavior and to help in the trajectory selection process to provide a social-appropriateness score for a potential trajectory. This paper presents an evaluation done both in simulation and utilizing real human interaction.

Designing Collaborator Robots for Highly-Dynamic Multi-Human, Multi-Robot Teams, Office of Naval Research (ONR) PI: Monica Nicolescu, co-PI: David Feil-Seifer, Amount: $656,511, April 1, 2016 - March 31, 2019

CHS: Small: Collaborative Research: Spatio-Temporal Situational Awareness in Large-Scale Disasters Using Low-Cost Unmanned Aerial Vehicles, National Science Foundation PI: David Feil-Seifer, Amount: $166,666, Jan. 1, 2016 - Dec. 31, 2017

DURIP: Humanoid Platforms for Human-Robot Collaboration, Office of Naval Research (ONR) PI: Monica Nicolescu, co-PI: David Feil-Seifer, Mircea Nicolescu, Amount: $312,000, May 1, 2014 - June 30, 2015