I am an Associate Professor and the director of the Safe, Confident, and Aligned Learning + Robotics Lab (SCALAR) in the College of Information and Computer Sciences at The University of Massachusetts Amherst. I am also a core member of the interdepartmental UMass robotics group, as well as an Adjunct Professor at the University of Texas at Austin.
The goal of my research is to enable robots and other learning agents to be deployed in the real world with minimal expert intervention. Toward this goal, we develop efficient learning algorithms that enforce safety constraints, provide performance guarantees, and align human and agent objectives. Thus, our work draws from both machine learning and robotics, including topics such as imitation learning, reinforcement learning, 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.
Representative Publications
Value Alignment and Reward Inference
-
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.
-
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.
-
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.
[Code] -
A. Jain and S. Niekum.
Efficient Hierarchical Robot Motion Planning Under Uncertainty and Hybrid Dynamics.
Conference on Robot Learning (CoRL), October 2018.
[Code] [Video]
-
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.
[Code] -
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]