Publications

We propose a demonstration of the Social Environment for Autonomous Navigation with Virtual Reality (VR) for advancing research in Human-Robot Interaction. In our demonstration, a user controls a virtual avatar in simulation and performs directed navigation tasks with a mobile robot in a warehouse environment. Our demonstration shows how researchers can leverage the immersive nature of VR to study robot navigation from a user-centered perspective in densely populated environments while avoiding physical safety concerns common with operating robots in the real world. This is important for studying interactions with robots driven by algorithms that are early in their development lifecycle.

@inproceedings{zhang2023sean,
title={SEAN-VR: An Immersive Virtual Reality Experience for Evaluating Social Robot Navigation},
author={Zhang, Qiping and Tsoi, Nathan and V{\'a}zquez, Marynel},
booktitle={Companion of the 2023 ACM/IEEE International Conference on Human-Robot Interaction},
year={2023}
}

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.

@article{tsoi2022sean2, 
author={Tsoi, Nathan and Xiang, Alec and Yu, Peter and Sohn, Samuel S. and Schwartz, Greg and Ramesh, Subashri and Hussein, Mohamed and Gupta, Anjali W. and Kapadia, Mubbasir and Vázquez, Marynel},
journal={IEEE Robotics and Automation Letters},
title={SEAN 2.0: Formalizing and Generating Social Situations for Robot Navigation},
year={2022},
pages={1-8},
doi={10.1109/LRA.2022.3196783}
}

Evaluation of social robot navigation inherently requires human input due to its qualitative nature. Motivated by the need to scale human evaluation, we propose a general method for deploying interactive, rich-client robotic simulations on the web. Prior approaches implement specific web-compatible simulators or provide tools to build a simulator for a specific study. Instead, our approach builds on standard Linux tools to share a graphical desktop with remote users. We leverage these tools to deploy simulators on the web that would typically be constrained to desktop computing environments. As an example implementation of our approach, we introduce the SEAN Experimental Platform (SEAN-EP). With SEAN-EP, remote users can virtually interact with a mobile robot in the Social Environment for Autonomous Navigation, without installing any software on their computer or needing specialized hardware. We validated that SEAN-EP could quickly scale the collection of human feedback and its usability through an online survey. In addition, we compared human feedback from participants that interacted with a robot using SEAN-EP with feedback obtained through a more traditional video survey. Our results suggest that human perceptions of robots may differ based on whether they interact with the robots in simulation or observe them in videos. Also, they suggest that people perceive the surveys with interactive simulations as less mentally demanding than video surveys.

@inproceedings{Tsoi_2021_Sean_EP,
author    = {Tsoi, Nathan and Hussein, Mohamed and Fugikawa, Olivia and Zhao, J. D. and V\'{a}zquez, Marynel},
title     = {An Approach to Deploy Interactive Robotic Simulators on the Web for HRI Experiments: Results in Social Robot Navigation},
booktitle = {2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
year      = {2021},
organization={IEEE}
}

Social navigation research is performed on a variety of robotic platforms, scenarios, and environments. Making comparisons between navigation algorithms is challenging because of the effort involved in building these systems and the diversity of platforms used by the community; nonetheless, evaluation is critical to understanding progress in the field. In a step towards reproducible evaluation of social navigation algorithms, we propose the Social Environment for Autonomous Navigation (SEAN). SEAN is a high visual fidelity, open source, and extensible social navigation simulation platform which includes a toolkit for evaluation of navigation algorithms. We demonstrate SEAN and its evaluation toolkit in two environments with dynamic pedestrians and using two different robots.

@inproceedings{Tsoi_2020_HAI,
author    = {Tsoi, Nathan and Hussein, Mohamed and Espinoza, Jeacy and Ruiz, Xavier and V\'{a}zquez, Marynel},
title     = {SEAN: Social Environment for Autonomous Navigation},
booktitle = {Proceedings of the 8th International Conference on Human-Agent Interaction (HAI)},
month     = {November},
year      = {2020}
}