I am an Assistant Professor and the director of the Personal Autonomous Robotics Lab (PeARL) in the Department of Computer Science at the University of Texas at Austin. I am also a core faculty member in the interdepartmental robotics group here at UT.
The goal of my research is to enable personal robots to be deployed in the home and workplace with minimal intervention by robotics experts. In settings such as these, robots do not operate in isolation, but have continual interactions with people and objects in the world. With this in mind, we focus on developing algorithms to solve problems that robot learners encounter in real-world interactive settings. Thus, our work draws roughly equally from both machine learning and robotics, including topics such as learning from demonstration, manipulation, probabilistic safety, human-robot interaction, and reinforcement learning.
Prior to joining UT Austin, I was a postdoctoral research fellow at the Carnegie Mellon Robotics Institute, working with Chris Atkeson. I received my PhD in September 2013 from the Department of Computer Science at the University of Massachusetts Amherst, working under the supervision of Andrew Barto.
Recent News
- 8/6/17 — New ICML 2017 paper now available, showing a novel and efficient method for off-policy evaluation: Data-Efficient Policy Evaluation Through Behavior Policy Search.
- 7/30/17 — Our team UT Austin Villa won third place in the 2017 Robocup@Home Domestic Standard Platform League in Nagoya, Japan. And we got a shiny trophy!
- 6/22/17 — We have five workshop papers at RSS 2017 on active inverse reinforcement learning, human mental modeling, incremental learning from demonstration, hybrid POMDP planning, and visually grounding spatial relationships.
- 6/14/17 — Two papers accepted to IROS 2017 on Viewpoint Selection for Visual Failure Detection and Error Correction for Brain-Computer Interfaces.
- 5/12/17 — Our AAMAS 2017 paper shows how to perform safe policy evaluation more efficienty: Bootstrapping with Models: Confidence Intervals for Off-Policy Evaluation.
