Speaker: Vaibhav Unhelkar
Time: 2:40 p.m.-3:30 p.m.
Abstract: We are steadily moving towards a future where humans work with robotic assistants, robot teammates, and even robotic tutors. Towards realizing this future, it is essential to train both robots and humans to work with each other. My research develops computational foundations for enabling this human-robot training. This talk will begin with the problem of training robots to work with humans. To address this problem, I will summarize recent imitation learning techniques – FAMM and BTIL – that explicitly model partial observability of human behavior. Coupled with POMDP solvers, these techniques enable robots to predict and adapt to human behavior during collaborative task execution. Second, I will summarize AI Teacher: an explainable AI framework for training humans to work with robots. By leveraging human’s natural ability to model others (Theory of Mind), the AI Teacher framework reduces the number of interactions it takes for humans to arrive at predictive models of robot behavior. The talk will conclude with implications of these techniques for human-robot collaboration.
Bio: Vaibhav Unhelkar is an Assistant Professor of Computer Science at Rice University, where he leads the Human-Centered AI and Robotics (HCAIR) research group. Unhelkar has developed algorithms to enable fluent human-robot collaboration and, with industry collaborators, deployed robots among humans. Ongoing research in his group includes development of algorithms and systems to model human behavior, train human-robot teams, and improve transparency of AI systems. Unhelkar received his doctorate in Autonomous Systems at MIT (2020) and completed his undergraduate education at IIT Bombay (2012). He serves as an Associate Editor for IEEE Robotics and Automation Letters and is the recipient of AAMAS 2022 Best Program Committee Member Award. Before joining Rice, Unhelkar worked as a robotics researcher at Google X, the Moonshot Factory