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.
Representative Publications
Imitation Learning
- D.S. Brown, W. Goo, P. Nagarajan, and S. Niekum. Extrapolating Beyond Suboptimal Demonstrations via Inverse Reinforcement Learning from Observations. International Conference on Machine Learning (ICML), June 2019.
-
S. Niekum, S. Osentoski, G.D. Konidaris, S. Chitta, B. Marthi, and A.G. Barto.
Learning Grounded Finite-State Representations from Unstructured Demonstrations. International Journal of Robotics Research (IJRR), Vol. 34(2), pages 131-157, February 2015.
-
D.S. Brown and S. Niekum.
Efficient Probabilistic Performance Bounds for Inverse Reinforcement Learning. AAAI Conference on Artificial Intelligence, February 2018. - J.P. Hanna, P. Stone, and S. Niekum. Bootstrapping with Models: Confidence Intervals for Off-Policy Evaluation. International Conference on Autonomous Agents and Multiagent Systems (AAMAS), May 2017.
- 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.
- J.P. Hanna, P. Stone, and S. Niekum. Bootstrapping with Models: Confidence Intervals for Off-Policy Evaluation. International Conference on Autonomous Agents and Multiagent Systems (AAMAS), May 2017.
- A. Jain and S. Niekum. Efficient Hierarchical Robot Motion Planning Under Uncertainty and Hybrid Dynamics. 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.
-
K. Hausman, S. Niekum, S. Osentoski, and G. Sukhatme.
Active Articulation Model Estimation through Interactive Perception.
IEEE International Conference on Robotics and Automation (ICRA), May 2015.
