This presents a framework for a novel Unified Socially-Aware Navigation (USAN) architecture and motivates it for Socially Assistive Robotics (SAR) applications. This 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 the 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 robots to cope with the changing dynamics of a situation, there is a need to detect changes in the interaction context.

We present a context classification pipeline to allow a robot to change its navigation strategy based on the observed social scenario. Most of the existing research uses different techniques to incorporate social norms into robot path planning for a single context. Methods that work for hallway behavior might not work for approaching people, and so on. We developed a high-level decision-making subsystem, a model-based context classifier, and a multi-objective optimization-based local planner to achieve socially-aware trajectories for autonomously sensed contexts. Our approach augments the navigation stack of Robot Operating System (ROS) utilizing machine learning and optimization tools. Using a context classification system, the robot can select social objectives that are later used by Pareto Concavity Elimination Transformation (PaCcET) based local planner to generate safe, comfortable, and socially-appropriate trajectories for its environment. Our method was tested and validated in multiple environments on a Pioneer mobile robot platform; results show that the robot was able to select and account for social objectives autonomously.

We also developed new scales for observing HRI that can measure the perceived social intelligence (PSI) of robots. We validated our PSI scale by evaluating our PaCcET-based local planner; a bystander experiment showed that people perceived robots with socially appropriate navigation strategies as more socially intelligent when compared to robots using traditional navigation strategies.

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

REU Site: Collaborative Human-Robot Interaction, National Science Foundation PI: David Feil-Seifer, co-PI: Shamik Sengupta, Amount: $360,000, Feb. 1, 2018 - Jan. 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