Extrapolating beyond suboptimal demonstrations via inverse reinforcement learning from observations D Brown, W Goo, P Nagarajan, S Niekum International Conference on Machine Learning, 783-792, 2019 | 391 | 2019 |
Better-than-Demonstrator Imitation Learning via Automatically-Ranked Demonstrations DS Brown, W Goo, S Niekum Conference on Robot Learning, 330-359, 2020 | 209 | 2020 |
Taxonomy-regularized semantic deep convolutional neural networks W Goo, J Kim, G Kim, SJ Hwang Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The …, 2016 | 46 | 2016 |
Self-supervised online reward shaping in sparse-reward environments F Memarian, W Goo, R Lioutikov, S Niekum, U Topcu 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2021 | 45 | 2021 |
One-shot learning of multi-step tasks from observation via activity localization in auxiliary video W Goo, S Niekum 2019 International Conference on Robotics and Automation (ICRA), 7755-7761, 2019 | 38 | 2019 |
A Ranking Game for Imitation Learning H Sikchi, A Saran, W Goo, S Niekum Transactions on Machine Learning Research (TMLR), January 2023., 2023 | 17 | 2023 |
Know Your Boundaries: The Advantage of Explicit Behavior Cloning in Offline RL W Goo, S Niekum arXiv preprint arXiv:2206.00695, 2022, 2022 | 15* | 2022 |
You Only Evaluate Once: a Simple Baseline Algorithm for Offline RL W Goo, S Niekum 5th Annual Conference on Robot Learning, 2021 | 15 | 2021 |
Local Nonparametric Meta-Learning W Goo, S Niekum arXiv preprint arXiv:2002.03272, 2020 | 4 | 2020 |
Imitation learning with auxiliary, suboptimal, and task-agnostic data W Goo | | 2022 |