Chelsea Finn

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Sergey LevineUC Berkeley, Physical IntelligenceDirección de correo verificada de eecs.berkeley.edu
Pieter AbbeelUC Berkeley | CovariantDirección de correo verificada de cs.berkeley.edu
Tianhe YuGoogle DeepMindDirección de correo verificada de google.com
Karol HausmanPhysical Intelligence, StanfordDirección de correo verificada de physicalintelligence.company
Frederik EbertUC BerkeleyDirección de correo verificada de berkeley.edu
Suraj NairPhysical IntelligenceDirección de correo verificada de physicalintelligence.company
Christopher D ManningProfessor of Computer Science and Linguistics, Stanford UniversityDirección de correo verificada de stanford.edu
Percy LiangAssociate Professor of Computer Science, Stanford UniversityDirección de correo verificada de cs.stanford.edu
Trevor DarrellProfessor of Computer Science, U.C. BerkeleyDirección de correo verificada de eecs.berkeley.edu
Mohammad BabaeizadehGoogle BrainDirección de correo verificada de google.com
Dumitru ErhanDirector of Research @ Google DeepMindDirección de correo verificada de google.com
Aravind RajeswaranMeta AI, UC BerkeleyDirección de correo verificada de cs.washington.edu
Allan ZhouGoogle DeepMindDirección de correo verificada de stanford.edu
Abhishek GuptaUniversity of WashingtonDirección de correo verificada de cs.washington.edu
Alex X. LeeResearch Scientist, Google DeepMindDirección de correo verificada de google.com
Benjamin EysenbachPrinceton UniversityDirección de correo verificada de princeton.edu
Dinesh JayaramanAssistant Professor, University of PennsylvaniaDirección de correo verificada de seas.upenn.edu
Anusha NagabandiUC BerkeleyDirección de correo verificada de berkeley.edu
Coline DevinDeepMindDirección de correo verificada de deepmind.com
Sham M KakadeHarvard UniversityDirección de correo verificada de seas.harvard.edu

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Chelsea Finn

Stanford University, Physical Intelligence

Dirección de correo verificada de cs.stanford.edu - Página principal

machine learning robotics reinforcement learning


Título Ordenar por citas Ordenar por año Ordenar por título	Citado por Citado por	Año
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	14103	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	4315	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	4184	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	1915	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	1784*	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	1470	2021
Unsupervised learning for physical interaction through video prediction C Finn, I Goodfellow, S Levine Advances in neural information processing systems 29, 2016	1269	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	1202	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	1152	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	1069	2020
Model-based reinforcement learning for atari L Kaiser, M Babaeizadeh, P Milos, B Osinski, RH Campbell, ... International Conference on Learning Representations (ICLR), 2020	1031	2020
Meta-learning with implicit gradients A Rajeswaran, C Finn, S Kakade, S Levine Neural Information Processing Systems (NeurIPS), 2019	941	2019
Deep visual foresight for planning robot motion C Finn, S Levine 2017 IEEE International Conference on Robotics and Automation (ICRA), 2786-2793, 2017	909	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	865	2020
Probabilistic model-agnostic meta-learning C Finn, K Xu, S Levine Neural Information Processing Systems (NeurIPS), 2018	844	2018
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	833*	2023
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	819	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	775*	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	752	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	740*	2016

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