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 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 imitation learning, reinforcement learning, probabilistic safety, manipulation, and human-robot interaction.
I am a recipient of the of the NSF CAREER Award, the AFOSR Young Investigator Award, and the UT Austin CNS Teaching Excellence Award.
A recent talk on "Scaling Probabilistically Safe Learning to Robotics", given virtually at Stanford on 4/17/20:
Representative Publications
Safe Learning
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D.S. Brown, R. Coleman, R. Srinivasan, and S. Niekum.
Safe Imitation Learning via Fast Bayesian Reward Inference from Preferences.
International Conference on Machine Learning (ICML), July 2020.
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D.S. Brown and S. Niekum.
Efficient Probabilistic Performance Bounds for Inverse Reinforcement Learning.
AAAI Conference on Artificial Intelligence, February 2018.
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D.S. Brown, W. Goo, and S. Niekum.
Better-than-Demonstrator Imitation Learning via Automatically-Ranked Demonstrations.
Conference on Robot Learning (CoRL), October 2019.
[Project Page and Code] -
D.S. Brown and S. Niekum.
Machine Teaching for Inverse Reinforcement Learning: Algorithms and Applications.
AAAI Conference on Artificial Intelligence, February 2019.
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A. Jain and S. Niekum.
Efficient Hierarchical Robot Motion Planning Under Uncertainty and Hybrid Dynamics.
Conference on Robot Learning (CoRL), October 2018.
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J.P. Hanna, P.S. Thomas, P. Stone, and S. Niekum.
Data-Efficient Policy Evaluation Through Behavior Policy Search.
International Conference on Machine Learning (ICML), August 2017.
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D.S. Brown, Y. Cui, and S. Niekum.
Risk-Aware Active Inverse Reinforcement Learning.
Conference on Robot Learning (CoRL), October 2018. -
Y. Cui and S. Niekum.
Active Reward Learning from Critiques.
IEEE International Conference on Robotics and Automation (ICRA), May 2018.
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A. Saran, E.S. Short, A.L. Thomaz, and S. Niekum.
Understanding Teacher Gaze Patterns for Robot Learning.
Conference on Robot Learning (CoRL), October 2019. -
P. Goyal, S. Niekum, and R. Mooney.
Using Natural Language for Reward Shaping in Reinforcement Learning.
International Joint Conference on Artificial Intelligence (IJCAI), August 2019.
