  This paper presents the framework for a novel Unified Socially-Aware
Navigation (USAN) architecture 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
for helping humans to feel comfortable and safe around robots; HRI studies have
shown the importance of SAN transcends 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 robots
intended for interactions with people. 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 the 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
architecture to SAN that can plan and execute trajectories for an autonomously
sensed interaction context that are human-friendly. Our approach augments the
navigation stack of the Robot Operating System (ROS) utilizing machine learning
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 USAN. We discuss our preliminary results and concrete
plans on putting the pieces together in achieving USAN.
