SEAN 2.0: Formalizing and Generating Social Situations for Robot Navigation
Nathan Tsoi, Alec Xiang, Peter Yu, Samuel S. Sohn, Greg Schwartz, Subashri Ramesh, Mohamed Hussein, Anjali W. Gupta, Mubbasir Kapadia, and Marynel Vázquez
IEEE Robotics and Automation Letters (RA-L) 2022
We present SEAN 2.0, an open-source system designed to advance social navigation via the training and benchmarking of navigation policies in varied social contexts. A key limitation of current social navigation research is that policies are often trained and evaluated considering only a few social contexts, which are fragmented across prior work. Inspired by work in psychology, we describe navigation context based on social situations, which encompass the robot task and environmental factors, and propose logic-based classifiers for five common examples. SEAN 2.0 allows a robot to experience these social situations via different methods for specifying and generating pedestrian motion, including a novel Behavior Graph method. Our experiments show that when data collected using the Behavior Graph method is used to learn a robot navigation policy, that policy outperforms others trained using alternative methods for pedestrian control. Also, social situations were found to be useful for understanding performance across social contexts. Other components of SEAN 2.0 include vision and depth sensors, several physical environments, different means of specifying robot tasks, and a range of evaluation metrics for social robot navigation. User feedback for SEAN 2.0 indicated that the system was “easier to navigate and more user friendly” than SEAN 1.0.