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Chelsea Finn
Chelsea Finn
Stanford University, Physical Intelligence
Geverifieerd e-mailadres voor cs.stanford.edu - Homepage
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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
149752017
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
50432021
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
43222016
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
29022023
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
2087*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
16142021
Unsupervised learning for physical interaction through video prediction
C Finn, I Goodfellow, S Levine
Advances in neural information processing systems 29, 2016
12822016
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
12792019
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
12482016
Gradient surgery for multi-task learning
T Yu, S Kumar, A Gupta, S Levine, K Hausman, C Finn
Neural Information Processing Systems (NeurIPS), 2020
12062020
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
1114*2023
Model-based reinforcement learning for atari
L Kaiser, M Babaeizadeh, P Milos, B Osinski, RH Campbell, ...
International Conference on Learning Representations (ICLR), 2020
10892020
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
10252022
Meta-learning with implicit gradients
A Rajeswaran, C Finn, S Kakade, S Levine
Neural Information Processing Systems (NeurIPS), 2019
9862019
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
9462020
Deep visual foresight for planning robot motion
C Finn, S Levine
2017 IEEE international conference on robotics and automation (ICRA), 2786-2793, 2017
9292017
Probabilistic model-agnostic meta-learning
C Finn, K Xu, S Levine
Neural Information Processing Systems (NeurIPS), 2018
8682018
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
8022019
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
801*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
762*2016
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Artikelen 1–20