Faculty profile 2
Dr. Niekum's research focuses on enabling personal robots to be deployed in the home and workplace with minimal intervention by robotics experts. To address this task, his group develops algorithms that allow robots to quickly and robustly characterize new tasks and environments autonomously, as well as be learn from human users. This work draws roughly equally from both machine learning and robotics, including topics such as imitation learning, manipulation, and reinforcement learning.
2018 NSF CAREER Award
D. 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.
J.P. Hanna, S. Niekum, and P. Stone. Importance Sampling Policy Evaluation with an Estimated Behavior Policy. International Conference on Machine Learning (ICML), June 2019.
D. Brown and S. Niekum. Machine Teaching for Inverse Reinforcement Learning: Algorithms and Applications. AAAI Conference on Artificial Intelligence, February 2019.
A. Jain and S. Niekum. Efficient Hierarchical Robot Motion Planning Under Uncertainty and Hybrid Dynamics. Conference on Robot Learning (CoRL), October 2018.
D. Brown and S. Niekum. Efficient Probabilistic Performance Bounds for Inverse Reinforcement Learning. AAAI Conference on Artificial Intelligence, February 2018.