| 6-dof grasping for target-driven object manipulation in clutter A Murali, A Mousavian, C Eppner, C Paxton, D Fox 2020 IEEE International Conference on Robotics and Automation (ICRA), 6232-6238, 2020 | 233 | 2020 |
| CoSTAR: Instructing collaborative robots with behavior trees and vision C Paxton, A Hundt, F Jonathan, K Guerin, GD Hager 2017 IEEE international conference on robotics and automation (ICRA), 564-571, 2017 | 193 | 2017 |
| Pre-Trained Language Models for Interactive Decision-Making S Li, X Puig, C Paxton, Y Du, C Wang, Y Fan, T Chen, DA Huang, ... arXiv preprint arXiv:2202.01771, 2022 | 165 | 2022 |
| Combining neural networks and tree search for task and motion planning in challenging environments C Paxton, V Raman, GD Hager, M Kobilarov Intelligent Robots and Systems (IROS), 2017 IEEE/RSJ International Conference on, 2017 | 144 | 2017 |
| Developing predictive models using electronic medical records: challenges and pitfalls C Paxton, A Niculescu-Mizil, S Saria AMIA Annual Symposium Proceedings 2013, 1109, 2013 | 137 | 2013 |
| A framework for end-user instruction of a robot assistant for manufacturing KR Guerin, C Lea, C Paxton, GD Hager 2015 IEEE international conference on robotics and automation (ICRA), 6167-6174, 2015 | 134 | 2015 |
| Task and motion planning with large language models for object rearrangement Y Ding, X Zhang, C Paxton, S Zhang 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2023 | 126 | 2023 |
| Online replanning in belief space for partially observable task and motion problems CR Garrett, C Paxton, T Lozano-Pérez, LP Kaelbling, D Fox 2020 IEEE International Conference on Robotics and Automation (ICRA), 5678-5684, 2020 | 122 | 2020 |
| A persistent spatial semantic representation for high-level natural language instruction execution V Blukis, C Paxton, D Fox, A Garg, Y Artzi Conference on Robot Learning, 706-717, 2022 | 119 | 2022 |
| Clip-fields: Weakly supervised semantic fields for robotic memory NMM Shafiullah, C Paxton, L Pinto, S Chintala, A Szlam arXiv preprint arXiv:2210.05663, 2022 | 111 | 2022 |
| Correcting robot plans with natural language feedback P Sharma, B Sundaralingam, V Blukis, C Paxton, T Hermans, A Torralba, ... arXiv preprint arXiv:2204.05186, 2022 | 87 | 2022 |
| Structformer: Learning spatial structure for language-guided semantic rearrangement of novel objects W Liu, C Paxton, T Hermans, D Fox 2022 International Conference on Robotics and Automation (ICRA), 6322-6329, 2022 | 86 | 2022 |
| "Good Robot!": Efficient Reinforcement Learning for Multi-Step Visual Tasks via Reward Shaping A Hundt, B Killeen, H Kwon, C Paxton, GD Hager arXiv preprint arXiv:1909.11730, 2019 | 84* | 2019 |
| Reactive human-to-robot handovers of arbitrary objects W Yang, C Paxton, A Mousavian, YW Chao, M Cakmak, D Fox 2021 IEEE International Conference on Robotics and Automation (ICRA), 3118-3124, 2021 | 78 | 2021 |
| Trajectory prediction of third-party objects using temporal logic and tree search M Kobilarov, T Caldwell, V Raman, C Paxton US Patent 10,671,076, 2020 | 77 | 2020 |
| StructDiffusion: Language-guided creation of physically-valid structures using unseen objects W Liu, Y Du, T Hermans, S Chernova, C Paxton arXiv preprint arXiv:2211.04604, 2022 | 73* | 2022 |
| Sornet: Spatial object-centric representations for sequential manipulation W Yuan, C Paxton, K Desingh, D Fox Conference on Robot Learning, 148-157, 2022 | 71 | 2022 |
| Nerp: Neural rearrangement planning for unknown objects AH Qureshi, A Mousavian, C Paxton, MC Yip, D Fox arXiv preprint arXiv:2106.01352, 2021 | 71 | 2021 |
| Representing robot task plans as robust logical-dynamical systems C Paxton, N Ratliff, C Eppner, D Fox 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2019 | 71 | 2019 |
| Visual robot task planning C Paxton, Y Barnoy, K Katyal, R Arora, GD Hager 2019 international conference on robotics and automation (ICRA), 8832-8838, 2019 | 71 | 2019 |
Dieter FoxUniversity of Washington and NVIDIAVerified email at cs.washington.edu
