| SymPy: symbolic computing in Python A Meurer, CP Smith, M Paprocki, O Čertík, SB Kirpichev, M Rocklin, ... PeerJ Computer Science 3, e103, 2017 | 1787 | 2017 |
| Search-based task planning with learned skill effect models for lifelong robotic manipulation J Liang, M Sharma, A LaGrassa, S Vats, S Saxena, O Kroemer 2022 International Conference on Robotics and Automation (ICRA), 6351-6357, 2022 | 34 | 2022 |
| Synergistic Scheduling of Learning and Allocation of Tasks in Human-robot Teams S Vats, O Kroemer, M Likhachev 2022 International Conference on Robotics and Automation (ICRA), 2789-2795, 2022 | 8 | 2022 |
| Efficient Recovery Learning using Model Predictive Meta-Reasoning S Vats, M Likhachev, O Kroemer 2023 IEEE International Conference on Robotics and Automation (ICRA), 7258-7264, 2023 | 6 | 2023 |
| Learning to avoid local minima in planning for static environments S Vats, V Narayanan, M Likhachev Proceedings of the International Conference on Automated Planning and …, 2017 | 6 | 2017 |
| SCALE: Causal Learning and Discovery of Robot Manipulation Skills using Simulation TE Lee*, S Vats*, S Girdhar, O Kroemer 7th Annual Conference on Robot Learning, 2023 | 4 | 2023 |
| Learning to use adaptive motion primitives in search-based planning for navigation R Sood, S Vats, M Likhachev 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2020 | 4 | 2020 |
| Multi-robot motion planning with diffusion models Y Shaoul, I Mishani, S Vats, J Li, M Likhachev arXiv preprint arXiv:2410.03072, 2024 | 3 | 2024 |
| Recoverychaining: Learning local recovery policies for robust manipulation S Vats, DK Jha, M Likhachev, O Kroemer, D Romeres arXiv preprint arXiv:2410.13979, 2024 | 1 | 2024 |
| Plan to Learn: Active Robot Learning by Planning S Vats Carnegie Mellon University, 2024 | | 2024 |
Oliver KroemerCarnegie Mellon University - The Robotics InstituteVerified email at andrew.cmu.edu
