Beginning Fall 2022 I will be moving to the College of Information and Computing Science at UMass Amherst, and therefore am not taking on any new students at UT Austin. If you wish to work with me, please apply here.
I am an Associate 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 robots to be deployed in the real world 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.
Announcement: I'm looking for a postdoc to work at the intersection of value alignment, safe learning, and robotic manipulation. For more info, see the application instructions.
Representative Publications
Value Alignment and Imitation Learning
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D.S. Brown, J. Schneider, A. Dragan, and S. Niekum.
Value Alignment Verification.
International Conference on Machine Learning (ICML), July 2021.
[Project Page and Code] -
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.
[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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M. Alshiekh, R. Bloem, R. Ehlers, B. Könighofer, S. Niekum, and U. Topcu.
Safe Reinforcement Learning via Shielding.
AAAI Conference on Artificial Intelligence, February 2018. -
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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A. Jain, R. Lioutikov, C. Chuck, and S. Niekum.
ScrewNet: Category-Independent Articulation Model Estimation From Depth Images Using Screw Theory.
IEEE International Conference on Robotics and Automation (ICRA), June 2021.
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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.
[Code] [Video]
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Y. Cui, Q. Zhang, A. Allievi, P. Stone, S. Niekum, and W. Knox.
The EMPATHIC Framework for Task Learning from Implicit Human Feedback.
Conference on Robot Learning (CoRL), November 2020.
[Project Page and Code] -
A. Saran, R. Zhang, E.S. Short, and S. Niekum.
Efficiently Guiding Imitation Learning Algorithms with Human Gaze.
International Conference on Autonomous Agents and Multiagent Systems (AAMAS), May 2021.
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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.
[Code]