Dr. Niekum is 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. He is also a core faculty member in the interdepartmental robotics group at UT.
Enabling personal robots to be deployed with minimal intervention by robotics experts
Machine learning and robotics
learning from demonstration, manipulation, time-series analysis, control theory, and reinforcement learning.
P.S. Thomas, S. Niekum, G. Theocharous, and G.D. Konidaris. December 2015. Policy Evaluation Using the Omega-Return. Advances in Neural Information Processing Systems.
S. Niekum, S. Osentoski, C.G. Atkeson, and A.G. Barto. May 2015. Online Bayesian Changepoint Detection for Articulated Motion Models. IEEE International Conference on Robotics and Automation.
K. Hausman, S. Niekum, S. Osentoski, and G. Sukhatme. May 2015. Active Articulation Model Estimation through Interactive Perception. IEEE International Conference on Robotics and Automation.
S. Niekum, S. Osentoski, G.D. Konidaris, S. Chitta, B. Marthi, and A.G. Barto. February 2015. Learning Grounded Finite-State Representations from Unstructured Demonstrations. International Journal of Robotics Research.
S. Niekum, S. Osentoski, S. Chitta, B. Marthi, and Andrew G. Barto. June 2013. Incremental Semantically Grounded Learning from Demonstration. Robotics: Science and Systems.