Chelsea Finn

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Sergey LevineUC Berkeley, Physical IntelligenceAdresse e-mail validée de eecs.berkeley.edu
Pieter AbbeelUC Berkeley | CovariantAdresse e-mail validée de cs.berkeley.edu
Tianhe YuGoogle DeepMindAdresse e-mail validée de google.com
Karol HausmanPhysical Intelligence, StanfordAdresse e-mail validée de physicalintelligence.company
Frederik EbertUC BerkeleyAdresse e-mail validée de berkeley.edu
Suraj NairPhysical IntelligenceAdresse e-mail validée de physicalintelligence.company
Christopher D ManningProfessor of Computer Science and Linguistics, Stanford UniversityAdresse e-mail validée de stanford.edu
Percy LiangAssociate Professor of Computer Science, Stanford UniversityAdresse e-mail validée de cs.stanford.edu
Trevor DarrellProfessor of Computer Science, U.C. BerkeleyAdresse e-mail validée de eecs.berkeley.edu
Mohammad BabaeizadehGoogle BrainAdresse e-mail validée de google.com
Dumitru ErhanDirector of Research @ Google DeepMindAdresse e-mail validée de google.com
Aravind RajeswaranMeta AI, UC BerkeleyAdresse e-mail validée de cs.washington.edu
Allan ZhouGoogle DeepMindAdresse e-mail validée de stanford.edu
Abhishek GuptaUniversity of WashingtonAdresse e-mail validée de cs.washington.edu
Alex X. LeeResearch Scientist, Google DeepMindAdresse e-mail validée de google.com
Benjamin EysenbachPrinceton UniversityAdresse e-mail validée de princeton.edu
Dinesh JayaramanAssistant Professor, University of PennsylvaniaAdresse e-mail validée de seas.upenn.edu
Anusha NagabandiUC BerkeleyAdresse e-mail validée de berkeley.edu
Coline DevinDeepMindAdresse e-mail validée de deepmind.com
Sham M KakadeHarvard UniversityAdresse e-mail validée de seas.harvard.edu

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Chelsea Finn

Stanford University, Physical Intelligence

Adresse e-mail validée de cs.stanford.edu - Page d'accueil

machine learning robotics reinforcement learning


Titre Trier par citations Trier par année Trier par titre	Citée par Citée par	Année
Model-agnostic meta-learning for fast adaptation of deep networks C Finn, P Abbeel, S Levine International Conference on Machine Learning (ICML), 1126-1135, 2017	14120	2017
On the opportunities and risks of foundation models R Bommasani, DA Hudson, E Adeli, R Altman, S Arora, S von Arx, ... arXiv preprint arXiv:2108.07258, 2021	4348	2021
End-to-end training of deep visuomotor policies S Levine, C Finn, T Darrell, P Abbeel Journal of Machine Learning Research 17 (1), 1334-1373, 2016	4185	2016
Direct preference optimization: Your language model is secretly a reward model R Rafailov, A Sharma, E Mitchell, S Ermon, CD Manning, C Finn Neural Information Processing Systems (NeurIPS), 2023	1948	2023
Do as I can, not as I say: Grounding language in robotic affordances M Ahn, A Brohan, N Brown, Y Chebotar, O Cortes, B David, C Finn, ... Conference on Robot Learning (CoRL), 2022	1765*	2022
Wilds: A benchmark of in-the-wild distribution shifts PW Koh, S Sagawa, H Marklund, SM Xie, M Zhang, A Balsubramani, ... International Conference on Machine Learning (ICML), 5637-5664, 2021	1473	2021
Unsupervised learning for physical interaction through video prediction C Finn, I Goodfellow, S Levine Advances in neural information processing systems 29, 2016	1270	2016
Guided cost learning: Deep inverse optimal control via policy optimization C Finn, S Levine, P Abbeel International Conference on Machine Learning (ICML), 49-58, 2016	1206	2016
Meta-world: A benchmark and evaluation for multi-task and meta reinforcement learning T Yu, D Quillen, Z He, R Julian, K Hausman, C Finn, S Levine Conference on Robot Learning (CoRL), 2019	1154	2019
Gradient surgery for multi-task learning T Yu, S Kumar, A Gupta, S Levine, K Hausman, C Finn Neural Information Processing Systems (NeurIPS), 2020	1074	2020
Model-based reinforcement learning for atari L Kaiser, M Babaeizadeh, P Milos, B Osinski, RH Campbell, ... International Conference on Learning Representations (ICLR), 2020	1033	2020
Meta-learning with implicit gradients A Rajeswaran, C Finn, S Kakade, S Levine Neural Information Processing Systems (NeurIPS), 2019	940	2019
Deep visual foresight for planning robot motion C Finn, S Levine 2017 IEEE International Conference on Robotics and Automation (ICRA), 2786-2793, 2017	911	2017
Mopo: Model-based offline policy optimization T Yu, G Thomas, L Yu, S Ermon, J Zou, S Levine, C Finn, T Ma Neural Information Processing Systems (NeurIPS), 2020	870	2020
Rt-2: Vision-language-action models transfer web knowledge to robotic control B Zitkovich, T Yu, S Xu, P Xu, T Xiao, F Xia, J Wu, P Wohlhart, S Welker, ... Conference on Robot Learning, 2165-2183, 2023	848*	2023
Probabilistic model-agnostic meta-learning C Finn, K Xu, S Levine Neural Information Processing Systems (NeurIPS), 2018	844	2018
RT-1: Robotics transformer for real-world control at scale A Brohan, N Brown, J Carbajal, Y Chebotar, J Dabis, C Finn, ... Robotics: Science and Systems (RSS), 2022	826	2022
Learning to adapt in dynamic, real-world environments through meta-reinforcement learning A Nagabandi, I Clavera, S Liu, RS Fearing, P Abbeel, S Levine, C Finn International Conference on Learning Representations (ICLR), 2019	776*	2019
Efficient off-policy meta-reinforcement learning via probabilistic context variables K Rakelly, A Zhou, D Quillen, C Finn, S Levine International Conference on Machine Learning (ICML), 2019	753	2019
Deep spatial autoencoders for visuomotor learning C Finn, XY Tan, Y Duan, T Darrell, S Levine, P Abbeel 2016 IEEE International Conference on Robotics and Automation (ICRA), 512-519, 2016	742*	2016

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