I am a computer scientist working at the intersection of robotics and machine learning. My research aims to advance the effectiveness and robustness of robotic systems that interact with humans, with the ultimate goal of making robotic platforms more useful in the real world. Currently, I am a PhD student at Yale University in robotics and a member of the Interactive Machines Group advised by Marynel Vázquez. Previously, I have done research at Stanford University in the Stanford Vision and Learning Lab under Silvio Savarese and have worked on machine learning and data engineering at Sequoia. For fun, I enjoy designing hardware and embedded systems programming.
Research
An Approach to Deploy Interactive Robotic Simulators on the Web for HRI Experiments: Results in Social Robot Navigation
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2021
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.
Improving the Robustness of Social Robot Navigation Systems
Companion of the 2020 ACM/IEEE International Conference on Human-Robot Interaction (HRI) 2021
Our aim is to advance the reliability of autonomous social navigation. We have researched how simulation may advance this goal via crowdsourcing. We recently proposed the Simulation Environment for Autonomous Navigation (SEAN) and deployed it at scale on the web to quickly collect data via the SEAN Experimental Platform (SEAN-EP). Using this platform, we studied participants' perceptions of a robot when seen in a video versus interacting with it in simulation.
Our current research builds on this prior work to make autonomous social navigation more reliable by classifying and automatically detecting navigation errors.
Challenges Deploying Robots During a Pandemic: An Effort to Fight Social Isolation Among Children
ACM/IEEE International Conference on Human-Robot Interaction (HRI) 2021
The practice of social distancing during the COVID-19 pandemic resulted in billions of people quarantined in their homes. In response, we designed and deployed VectorConnect, a robot teleoperation system intended to help combat the effects of social distancing in children during the pandemic. VectorConnect uses the off-the-shelf Vector robot to allow its users to engage in physical play while being geographically separated. We distributed the system to hundreds of users in a matter of weeks. This paper details the development and deployment of the system, our accomplishments, and the obstacles encountered throughout this process. Also, it provides recommendations to best facilitate similar deployments in the future. We hope that this case study about Human-Robot Interaction practice serves as inspiration to innovate in times of global crises.
An End-to-End Approach for Training Neural Network Binary Classifiers on Metrics Based on the Confusion Matrix
While neural network binary classifiers are often evaluated on metrics such as Accuracy and $F_1$-Score, they are commonly trained with a cross-entropy objective. How can this training-testing gap be addressed? While specific techniques have been adopted to optimize certain confusion matrix based metrics, it is challenging or impossible in some cases to generalize the techniques to other metrics. Adversarial learning approaches have also been proposed to optimize networks via confusion matrix based metrics, but they tend to be much slower than common training methods. In this work, we propose to approximate the Heaviside step function, typically used to compute confusion matrix based metrics, to render these metrics amenable to gradient descent. Our extensive experiments show the effectiveness of our end-to-end approach for binary classification in several domains.
SEAN: Social Environment for Autonomous Navigation
Proceedings of the 8th International Conference on Human-Agent Interaction
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.
Improving Social Awareness Through DANTE: Deep Affinity Network for Clustering Conversational Interactants
Proceedings of the ACM on Human-Computer Interaction
We propose a data-driven approach to detect conversational groups by identifying spatial arrangements typical of these focused social encounters. Our approach uses a novel Deep Affinity Networkto predict the likelihood that two individuals in a scene are part of the same conversational group, considering their social context. The predicted pair-wise affinities are then used in a graph clustering framework to identify both small (e.g., dyads) and large groups. The results from our evaluation on multiple, established benchmarks suggest that combining powerful deep learning methods with classical clustering techniques can improve the detection of conversational groups in comparison to prior approaches. Finally, we demonstrate the practicality of our approach in a human-robot interaction scenario. Our efforts show that our work advances group detection not only in theory, but also in practice.
Prompting Prosocial Human Interventions in Response to Robot Mistreatment
ACM/IEEE International Conference on Human-Robot Interaction (HRI) 2020
Inspired by the benefits of human prosocial behavior, we explore whether prosocial behavior can be extended to a Human-Robot Interaction (HRI) context. More specifically, we study whether robots can induce prosocial behavior in humans through a 1x2 between-subjects user study ($N=30$) in which a confederate abused a robot. Through this study, we investigated whether the emotional reactions of a group of bystander robots could motivate a human to intervene in response to robot abuse. Our results show that participants were more likely to prosocially intervene when the bystander robots expressed sadness in response to the abuse as opposed to when they ignored these events, despite participants reporting similar perception of robot mistreatment and levels of empathy for the abused robot. Our findings demonstrate possible effects of group social influence through emotional cues by robots in human-robot interaction. They reveal a need for further research regarding human prosocial behavior within HRI.
Generalized Intersection over Union
Computer Vision and Pattern Recognition (CVPR) 2019
Intersection over Union (IoU) is the most popular evaluation metric used in the object detection benchmarks. However, there is a gap between optimizing the commonly used distance losses for regressing the parameters of a bounding box and maximizing this metric value. The optimal objective for a metric is the metric itself. In the case of axis-aligned 2D bounding boxes, it can be shown that $IoU$ can be directly used as a regression loss. However, $IoU$ has a plateau making it infeasible to optimize in the case of non-overlapping bounding boxes. In this paper, we address the weaknesses of $IoU$ by introducing a generalized version as both a new loss and a new metric. By incorporating this generalized $IoU$ ($GIoU$) as a loss into the state-of-the art object detection frameworks, we show a consistent improvement on their performance using both the standard, $IoU$ based, and new, $GIoU$ based, performance measures on popular object detection benchmarks such as PASCAL VOC and MS COCO.
Awards
Nathan Hale Associates: Teresa and Joshy Joseph Scholar
The Nathan Hale Associates program, founded in 1994, recognizes the many generous donors whose leadership annual gifts make possible the dynamic, diverse, and creative environment that defines the Yale experience.
HRI 2021 Best Paper Award Candidate
For the work: Challenges Deploying Robots During a Pandemic: An Effort to Fight Social Isolation Among Children by N. Tsoi, J. Connolly, E. Adéníran, A. Hansen, K. T. Pineda, T. Adamson, S. Thompson, R. Ramnauth, M. Vázquez, B. Scassellati
HAI 2020 Best Poster Award - Runner Up
For the work: SEAN: Social Environment for Autonomous Navigation by N. Tsoi, M. Hussein, J. Espinoza, X. Ruiz, and M. Vázquez
Alan J. Perlis Graduate Fellowship Recipient
This fellowship was established at Yale in 2006 through generous gifts from various donors in honor of Professor Alan J. Perlis (1922–1990), a pioneer of programming language research, the first winner of the Association for Computing Machinery’s (ACM) Turing Award, and the founding chair of Yale’s Computer Science Department.
Projects
Robots For Good: Fighting Social Isolation with Robots
Robotic telepresence for elementary-age children during social distancing.
Multi-sourced 2D and 3D Sensor Fusion and Person Tracking Pipeline
For research in imitation learning, creating motion policies for social navigation
Service and Activities
YHACK 2019 Final Judge