This paper presents a novel architecture to attain a Unified Planner for Socially-aware Navigation (UP-SAN) and explains its need in Socially Assistive Robotics (SAR) applications. Our approach emphasizes interpersonal distance and how spatial communication can be used to build a unified planner for a human-robot collaborative environment. Socially-Aware Navigation (SAN) is vital to make humans feel comfortable and safe around robots, HRI studies have show that the importance of SAN transcendent safety and comfort. SAN plays a crucial role in perceived intelligence, sociability and social capacity of the robot thereby increasing the acceptance of the robots in public places. Human environments are very dynamic and pose serious social challenges to the robots indented for human interactions. For the robots to cope with the changing dynamics of a situation, there is a need to infer intent and detect changes in the interaction context. SAN has gained immense interest in the social robotics community; to the best of our knowledge, however, there is no planner that can adapt to different interaction contexts spontaneously after autonomously sensing that context. Most of the recent efforts involve social path planning for a single context. In this work, we propose a novel approach for a Unified Planner for SAN that can plan and execute trajectories that are human-friendly for an autonomously sensed interaction context. Our approach augments the navigation stack of Robot Operating System (ROS) utilizing machine learn- ing and optimization tools. We modified the ROS navigation stack using a machine learning-based context classifier and a PaCcET based local planner for us to achieve the goals of UP- SAN. We discuss our preliminary results and concrete plans on putting the pieces together in achieving UP-SAN.

CHS: Small: Socially-Aware Navigation, National Science Foundation PI: David Feil-Seifer, co-PI: Monica Nicolescu, Amount: $500,000, Sept. 1, 2017 - May 31, 2022

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

Infrastructure For Socially-Aware Navigation For Long-Term Human-Robot Interaction, NASA EPSCoR PI: David Feil-Seifer, co-PI: Kimberly Barchard, Amount: $30,416, July 1, 2017 - April 9, 2018

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