Chelsea Finn

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Sergey LevineUC Berkeley, Physical IntelligencePatvirtintas el. paštas eecs.berkeley.edu
Pieter AbbeelUC Berkeley | CovariantPatvirtintas el. paštas cs.berkeley.edu
Tianhe YuGoogle DeepMindPatvirtintas el. paštas google.com
Karol HausmanPhysical Intelligence, StanfordPatvirtintas el. paštas physicalintelligence.company
Frederik EbertUC BerkeleyPatvirtintas el. paštas berkeley.edu
Suraj NairPhysical IntelligencePatvirtintas el. paštas physicalintelligence.company
Christopher D ManningProfessor of Computer Science and Linguistics, Stanford UniversityPatvirtintas el. paštas stanford.edu
Percy LiangAssociate Professor of Computer Science, Stanford UniversityPatvirtintas el. paštas cs.stanford.edu
Trevor DarrellProfessor of Computer Science, U.C. BerkeleyPatvirtintas el. paštas eecs.berkeley.edu
Mohammad BabaeizadehGoogle BrainPatvirtintas el. paštas google.com
Dumitru ErhanDirector of Research @ Google DeepMindPatvirtintas el. paštas google.com
Aravind RajeswaranMeta AI, UC BerkeleyPatvirtintas el. paštas cs.washington.edu
Allan ZhouGoogle DeepMindPatvirtintas el. paštas stanford.edu
Abhishek GuptaUniversity of WashingtonPatvirtintas el. paštas cs.washington.edu
Alex X. LeeResearch Scientist, Google DeepMindPatvirtintas el. paštas google.com
Benjamin EysenbachPrinceton UniversityPatvirtintas el. paštas princeton.edu
Dinesh JayaramanAssistant Professor, University of PennsylvaniaPatvirtintas el. paštas seas.upenn.edu
Anusha NagabandiUC BerkeleyPatvirtintas el. paštas berkeley.edu
Coline DevinDeepMindPatvirtintas el. paštas deepmind.com
Sham M KakadeHarvard UniversityPatvirtintas el. paštas seas.harvard.edu

Stebėti

Chelsea Finn

Stanford University, Physical Intelligence

Patvirtintas el. paštas cs.stanford.edu - Pagrindinis puslapis

machine learning robotics reinforcement learning


Pavadinimas Rūšiuoti pagal šaltinius Rūšiuoti pagal metus Rūšiuoti pagal pavadinimą	Cituota Cituota	Metai
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	14383	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	4533	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	4217	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	2119	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	1872*	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	1505	2021
Unsupervised learning for physical interaction through video prediction C Finn, I Goodfellow, S Levine Advances in neural information processing systems 29, 2016	1275	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	1219	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	1184	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	1109	2020
Model-based reinforcement learning for atari L Kaiser, M Babaeizadeh, P Milos, B Osinski, RH Campbell, ... International Conference on Learning Representations (ICLR), 2020	1057	2020
Meta-learning with implicit gradients A Rajeswaran, C Finn, S Kakade, S Levine Neural Information Processing Systems (NeurIPS), 2019	956	2019
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	925*	2023
Deep visual foresight for planning robot motion C Finn, S Levine 2017 IEEE International Conference on Robotics and Automation (ICRA), 2786-2793, 2017	917	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	889	2020
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	883	2022
Probabilistic model-agnostic meta-learning C Finn, K Xu, S Levine Neural Information Processing Systems (NeurIPS), 2018	848	2018
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	788*	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	766	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	747*	2016

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