Chris Paxton
Chris Paxton
Meta AI
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Zitiert von
Zitiert von
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
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
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
Developing predictive models using electronic medical records: challenges and pitfalls
C Paxton, A Niculescu-Mizil, S Saria
AMIA Annual Symposium Proceedings 2013, 1109, 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
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
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
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
"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
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
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
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
Nerp: Neural rearrangement planning for unknown objects
AH Qureshi, A Mousavian, C Paxton, MC Yip, D Fox
arXiv preprint arXiv:2106.01352, 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
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
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
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
Sornet: Spatial object-centric representations for sequential manipulation
W Yuan, C Paxton, K Desingh, D Fox
Conference on Robot Learning, 148-157, 2022
Task and motion planning with large language models for object rearrangement
Y Ding, X Zhang, C Paxton, S Zhang
arXiv preprint arXiv:2303.06247, 2023
An incremental approach to learning generalizable robot tasks from human demonstration
C Paxton, GD Hager, L Bascetta
2015 IEEE international conference on robotics and automation (ICRA), 5616-5621, 2015
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