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2020 – today
- 2023
[c28]Lantao Yu, Tianhe Yu, Jiaming Song, Willie Neiswanger, Stefano Ermon:
Offline Imitation Learning with Suboptimal Demonstrations via Relaxed Distribution Matching. AAAI 2023: 11016-11024- [c27]Danny Driess, Fei Xia, Mehdi S. M. Sajjadi, Corey Lynch, Aakanksha Chowdhery, Brian Ichter, Ayzaan Wahid, Jonathan Tompson, Quan Vuong, Tianhe Yu, Wenlong Huang, Yevgen Chebotar, Pierre Sermanet, Daniel Duckworth, Sergey Levine, Vincent Vanhoucke, Karol Hausman, Marc Toussaint, Klaus Greff, Andy Zeng, Igor Mordatch, Pete Florence:
PaLM-E: An Embodied Multimodal Language Model. ICML 2023: 8469-8488 - [c26]Gaoyue Zhou, Victoria Dean, Mohan Kumar Srirama, Aravind Rajeswaran, Jyothish Pari, Kyle Hatch, Aryan Jain, Tianhe Yu, Pieter Abbeel, Lerrel Pinto, Chelsea Finn, Abhinav Gupta:
Train Offline, Test Online: A Real Robot Learning Benchmark. ICRA 2023: 9197-9203 - [c25]Kyle Beltran Hatch, Benjamin Eysenbach, Rafael Rafailov, Tianhe Yu, Ruslan Salakhutdinov, Sergey Levine, Chelsea Finn:
Contrastive Example-Based Control. L4DC 2023: 155-169 - [c24]Anthony Brohan, Noah Brown, Justice Carbajal, Yevgen Chebotar, Joseph Dabis, Chelsea Finn, Keerthana Gopalakrishnan, Karol Hausman, Alexander Herzog, Jasmine Hsu, Julian Ibarz, Brian Ichter, Alex Irpan, Tomas Jackson, Sally Jesmonth, Nikhil J. Joshi, Ryan Julian, Dmitry Kalashnikov, Yuheng Kuang, Isabel Leal, Kuang-Huei Lee, Sergey Levine, Yao Lu, Utsav Malla, Deeksha Manjunath, Igor Mordatch, Ofir Nachum, Carolina Parada, Jodilyn Peralta, Emily Perez, Karl Pertsch, Jornell Quiambao, Kanishka Rao, Michael S. Ryoo, Grecia Salazar, Pannag R. Sanketi, Kevin Sayed, Jaspiar Singh, Sumedh Sontakke, Austin Stone, Clayton Tan, Huong T. Tran, Vincent Vanhoucke, Steve Vega, Quan Vuong, Fei Xia, Ted Xiao, Peng Xu, Sichun Xu, Tianhe Yu, Brianna Zitkovich:
RT-1: Robotics Transformer for Real-World Control at Scale. Robotics: Science and Systems 2023 - [c23]Tianhe Yu, Ted Xiao, Jonathan Tompson, Austin Stone, Su Wang, Anthony Brohan, Jaspiar Singh, Clayton Tan, Dee M, Jodilyn Peralta, Karol Hausman, Brian Ichter, Fei Xia:
Scaling Robot Learning with Semantically Imagined Experience. Robotics: Science and Systems 2023 - [i28]Tianhe Yu, Ted Xiao, Austin Stone, Jonathan Tompson, Anthony Brohan, Su Wang, Jaspiar Singh, Clayton Tan, Dee M, Jodilyn Peralta, Brian Ichter, Karol Hausman, Fei Xia:
Scaling Robot Learning with Semantically Imagined Experience. CoRR abs/2302.11550 (2023) - [i27]Lantao Yu, Tianhe Yu, Jiaming Song, Willie Neiswanger, Stefano Ermon:
Offline Imitation Learning with Suboptimal Demonstrations via Relaxed Distribution Matching. CoRR abs/2303.02569 (2023) - [i26]Danny Driess, Fei Xia, Mehdi S. M. Sajjadi, Corey Lynch, Aakanksha Chowdhery, Brian Ichter, Ayzaan Wahid, Jonathan Tompson, Quan Vuong, Tianhe Yu, Wenlong Huang, Yevgen Chebotar, Pierre Sermanet, Daniel Duckworth, Sergey Levine, Vincent Vanhoucke, Karol Hausman, Marc Toussaint, Klaus Greff, Andy Zeng, Igor Mordatch, Pete Florence:
PaLM-E: An Embodied Multimodal Language Model. CoRR abs/2303.03378 (2023) - [i25]Gaoyue Zhou, Victoria Dean, Mohan Kumar Srirama, Aravind Rajeswaran, Jyothish Pari, Kyle Hatch, Aryan Jain, Tianhe Yu, Pieter Abbeel, Lerrel Pinto, Chelsea Finn, Abhinav Gupta:
Train Offline, Test Online: A Real Robot Learning Benchmark. CoRR abs/2306.00942 (2023) - [i24]Kyle Hatch, Benjamin Eysenbach, Rafael Rafailov, Tianhe Yu, Ruslan Salakhutdinov, Sergey Levine, Chelsea Finn:
Contrastive Example-Based Control. CoRR abs/2307.13101 (2023) - [i23]Anthony Brohan, Noah Brown, Justice Carbajal, Yevgen Chebotar, Xi Chen, Krzysztof Choromanski, Tianli Ding, Danny Driess, Avinava Dubey, Chelsea Finn, Pete Florence, Chuyuan Fu, Montse Gonzalez Arenas, Keerthana Gopalakrishnan, Kehang Han, Karol Hausman, Alexander Herzog, Jasmine Hsu, Brian Ichter, Alex Irpan, Nikhil J. Joshi, Ryan Julian, Dmitry Kalashnikov, Yuheng Kuang, Isabel Leal, Lisa Lee, Tsang-Wei Edward Lee, Sergey Levine, Yao Lu, Henryk Michalewski, Igor Mordatch, Karl Pertsch, Kanishka Rao, Krista Reymann, Michael S. Ryoo, Grecia Salazar, Pannag Sanketi, Pierre Sermanet, Jaspiar Singh, Anikait Singh, Radu Soricut, Huong T. Tran, Vincent Vanhoucke, Quan Vuong, Ayzaan Wahid, Stefan Welker, Paul Wohlhart, Jialin Wu, Fei Xia, Ted Xiao, Peng Xu, Sichun Xu, Tianhe Yu, Brianna Zitkovich:
RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control. CoRR abs/2307.15818 (2023) - [i22]Yevgen Chebotar, Quan Vuong, Alex Irpan, Karol Hausman, Fei Xia, Yao Lu, Aviral Kumar, Tianhe Yu, Alexander Herzog, Karl Pertsch, Keerthana Gopalakrishnan, Julian Ibarz, Ofir Nachum, Sumedh Sontakke, Grecia Salazar, Huong T. Tran, Jodilyn Peralta, Clayton Tan, Deeksha Manjunath, Jaspiar Singh, Brianna Zitkovich, Tomas Jackson, Kanishka Rao, Chelsea Finn, Sergey Levine:
Q-Transformer: Scalable Offline Reinforcement Learning via Autoregressive Q-Functions. CoRR abs/2309.10150 (2023) - 2022
- [j4]Tianhe Yu
, Ming Zhu, Haiming Chen:
Single image dehazing based on multi-scale segmentation and deep learning. Mach. Vis. Appl. 33(2): 33 (2022) - [c22]Kaylee Burns, Tianhe Yu, Chelsea Finn, Karol Hausman:
Offline Reinforcement Learning at Multiple Frequencies. CoRL 2022: 2041-2051 - [c21]Tianhe Yu, Aviral Kumar, Yevgen Chebotar, Karol Hausman, Chelsea Finn, Sergey Levine:
How to Leverage Unlabeled Data in Offline Reinforcement Learning. ICML 2022: 25611-25635 - [c20]Xi Chen, Ali Ghadirzadeh, Tianhe Yu, Jianhao Wang, Alex Yuan Gao, Wenzhe Li, Liang Bin, Chelsea Finn, Chongjie Zhang:
LAPO: Latent-Variable Advantage-Weighted Policy Optimization for Offline Reinforcement Learning. NeurIPS 2022 - [i21]Tianhe Yu, Aviral Kumar, Yevgen Chebotar, Karol Hausman, Chelsea Finn, Sergey Levine:
How to Leverage Unlabeled Data in Offline Reinforcement Learning. CoRR abs/2202.01741 (2022) - [i20]Xi Chen, Ali Ghadirzadeh, Tianhe Yu, Yuan Gao, Jianhao Wang, Wenzhe Li, Bin Liang, Chelsea Finn, Chongjie Zhang:
Latent-Variable Advantage-Weighted Policy Optimization for Offline RL. CoRR abs/2203.08949 (2022) - [i19]Kaylee Burns, Tianhe Yu, Chelsea Finn, Karol Hausman:
Offline Reinforcement Learning at Multiple Frequencies. CoRR abs/2207.13082 (2022) - [i18]Anthony Brohan, Noah Brown, Justice Carbajal, Yevgen Chebotar, Joseph Dabis, Chelsea Finn, Keerthana Gopalakrishnan, Karol Hausman, Alexander Herzog, Jasmine Hsu, Julian Ibarz, Brian Ichter, Alex Irpan, Tomas Jackson, Sally Jesmonth, Nikhil J. Joshi, Ryan Julian, Dmitry Kalashnikov, Yuheng Kuang, Isabel Leal, Kuang-Huei Lee, Sergey Levine, Yao Lu, Utsav Malla, Deeksha Manjunath, Igor Mordatch, Ofir Nachum, Carolina Parada, Jodilyn Peralta, Emily Perez, Karl Pertsch, Jornell Quiambao, Kanishka Rao, Michael S. Ryoo, Grecia Salazar, Pannag Sanketi, Kevin Sayed, Jaspiar Singh, Sumedh Sontakke, Austin Stone, Clayton Tan, Huong T. Tran, Vincent Vanhoucke, Steve Vega, Quan Vuong, Fei Xia, Ted Xiao, Peng Xu, Sichun Xu, Tianhe Yu, Brianna Zitkovich:
RT-1: Robotics Transformer for Real-World Control at Scale. CoRR abs/2212.06817 (2022) - 2021
- [j3]Tianhe Yu, Ming Zhu:
Image Enhancement Algorithm Based on Image Spatial Domain Segmentation. Comput. Informatics 40(6) (2021) - [j2]Tianhe Yu, Chengdong Wang, Xiao Liu, Ming Zhu:
Adaptive superpixel-based multi-object pedestrian recognition. Mach. Vis. Appl. 32(1): 16 (2021) - [c19]Rafael Rafailov, Tianhe Yu, Aravind Rajeswaran, Chelsea Finn:
Offline Reinforcement Learning from Images with Latent Space Models. L4DC 2021: 1154-1168 - [c18]Rafael Rafailov, Tianhe Yu, Aravind Rajeswaran, Chelsea Finn:
Visual Adversarial Imitation Learning using Variational Models. NeurIPS 2021: 3016-3028 - [c17]Tianhe Yu, Aviral Kumar, Yevgen Chebotar, Karol Hausman, Sergey Levine, Chelsea Finn:
Conservative Data Sharing for Multi-Task Offline Reinforcement Learning. NeurIPS 2021: 11501-11516 - [c16]Chris Fifty, Ehsan Amid, Zhe Zhao, Tianhe Yu, Rohan Anil, Chelsea Finn:
Efficiently Identifying Task Groupings for Multi-Task Learning. NeurIPS 2021: 27503-27516 - [c15]Tianhe Yu, Aviral Kumar, Rafael Rafailov, Aravind Rajeswaran, Sergey Levine, Chelsea Finn:
COMBO: Conservative Offline Model-Based Policy Optimization. NeurIPS 2021: 28954-28967 - [i17]Tianhe Yu, Aviral Kumar, Rafael Rafailov, Aravind Rajeswaran, Sergey Levine, Chelsea Finn:
COMBO: Conservative Offline Model-Based Policy Optimization. CoRR abs/2102.08363 (2021) - [i16]Rafael Rafailov, Tianhe Yu, Aravind Rajeswaran, Chelsea Finn:
Visual Adversarial Imitation Learning using Variational Models. CoRR abs/2107.08829 (2021) - [i15]Christopher Fifty, Ehsan Amid, Zhe Zhao, Tianhe Yu, Rohan Anil, Chelsea Finn:
Efficiently Identifying Task Groupings for Multi-Task Learning. CoRR abs/2109.04617 (2021) - [i14]Tianhe Yu, Aviral Kumar, Yevgen Chebotar, Karol Hausman, Sergey Levine, Chelsea Finn:
Conservative Data Sharing for Multi-Task Offline Reinforcement Learning. CoRR abs/2109.08128 (2021) - 2020
- [c14]Kefan Dong, Yuping Luo, Tianhe Yu, Chelsea Finn, Tengyu Ma:
On the Expressivity of Neural Networks for Deep Reinforcement Learning. ICML 2020: 2627-2637 - [c13]Tianhe Yu, Saurabh Kumar, Abhishek Gupta, Sergey Levine, Karol Hausman, Chelsea Finn:
Gradient Surgery for Multi-Task Learning. NeurIPS 2020 - [c12]Tianhe Yu, Garrett Thomas, Lantao Yu, Stefano Ermon, James Y. Zou, Sergey Levine, Chelsea Finn, Tengyu Ma:
MOPO: Model-based Offline Policy Optimization. NeurIPS 2020 - [i13]Tianhe Yu, Saurabh Kumar, Abhishek Gupta, Sergey Levine, Karol Hausman, Chelsea Finn:
Gradient Surgery for Multi-Task Learning. CoRR abs/2001.06782 (2020) - [i12]Tianhe Yu, Garrett Thomas, Lantao Yu, Stefano Ermon, James Zou, Sergey Levine, Chelsea Finn, Tengyu Ma:
MOPO: Model-based Offline Policy Optimization. CoRR abs/2005.13239 (2020) - [i11]Christopher Fifty, Ehsan Amid, Zhe Zhao, Tianhe Yu, Rohan Anil, Chelsea Finn:
Measuring and Harnessing Transference in Multi-Task Learning. CoRR abs/2010.15413 (2020) - [i10]Tianhe Yu, Xinyang Geng, Chelsea Finn, Sergey Levine:
Variable-Shot Adaptation for Online Meta-Learning. CoRR abs/2012.07769 (2020) - [i9]Rafael Rafailov, Tianhe Yu, Aravind Rajeswaran, Chelsea Finn:
Offline Reinforcement Learning from Images with Latent Space Models. CoRR abs/2012.11547 (2020)
2010 – 2019
- 2019
- [c11]Tianhe Yu, Deirdre Quillen, Zhanpeng He, Ryan Julian, Karol Hausman, Chelsea Finn, Sergey Levine:
Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning. CoRL 2019: 1094-1100 - [c10]Tianhe Yu, Pieter Abbeel, Sergey Levine, Chelsea Finn:
One-Shot Composition of Vision-Based Skills from Demonstration. IROS 2019: 2643-2650 - [c9]Lantao Yu, Tianhe Yu, Chelsea Finn, Stefano Ermon:
Meta-Inverse Reinforcement Learning with Probabilistic Context Variables. NeurIPS 2019: 11749-11760 - [c8]Tianhe Yu, Gleb Shevchuk, Dorsa Sadigh, Chelsea Finn:
Unsupervised Visuomotor Control through Distributional Planning Networks. Robotics: Science and Systems 2019 - [i8]Tianhe Yu, Gleb Shevchuk, Dorsa Sadigh, Chelsea Finn:
Unsupervised Visuomotor Control through Distributional Planning Networks. CoRR abs/1902.05542 (2019) - [i7]Lantao Yu, Tianhe Yu, Chelsea Finn, Stefano Ermon:
Meta-Inverse Reinforcement Learning with Probabilistic Context Variables. CoRR abs/1909.09314 (2019) - [i6]Tianhe Yu, Deirdre Quillen, Zhanpeng He, Ryan Julian, Karol Hausman, Chelsea Finn, Sergey Levine:
Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning. CoRR abs/1910.10897 (2019) - 2018
- [b1]Tianhe Yu:
SMEM++: A Pipelined and Time-Multiplexed SMEM Seeding Accelerator for Genome Sequencing. University of California, Los Angeles, USA, 2018 - [c7]Jason Cong, Licheng Guo, Po-Tsang Huang, Peng Wei, Tianhe Yu:
SMEM++: A Pipelined and Time-Multiplexed SMEM Seeding Accelerator for DNA Sequencing. FCCM 2018: 206 - [c6]Jason Cong, Licheng Guo, Po-Tsang Huang, Peng Wei, Tianhe Yu:
SMEM++: A Pipelined and Time-Multiplexed SMEM Seeding Accelerator for Genome Sequencing. FPL 2018: 210-214 - [c5]Tianhe Yu, Chelsea Finn, Annie Xie, Sudeep Dasari, Tianhao Zhang, Pieter Abbeel, Sergey Levine:
One-Shot Imitation from Observing Humans via Domain-Adaptive Meta-Learning. ICLR (Workshop) 2018 - [c4]Tianhe Yu, Chelsea Finn, Sudeep Dasari, Annie Xie, Tianhao Zhang, Pieter Abbeel, Sergey Levine:
One-Shot Imitation from Observing Humans via Domain-Adaptive Meta-Learning. Robotics: Science and Systems 2018 - [i5]Tianhe Yu, Chelsea Finn, Annie Xie, Sudeep Dasari, Tianhao Zhang, Pieter Abbeel, Sergey Levine:
One-Shot Imitation from Observing Humans via Domain-Adaptive Meta-Learning. CoRR abs/1802.01557 (2018) - [i4]Tianhe Yu, Pieter Abbeel, Sergey Levine, Chelsea Finn:
One-Shot Hierarchical Imitation Learning of Compound Visuomotor Tasks. CoRR abs/1810.11043 (2018) - 2017
- [j1]Richard Zhang, Jun-Yan Zhu, Phillip Isola, Xinyang Geng, Angela S. Lin, Tianhe Yu, Alexei A. Efros:
Real-time user-guided image colorization with learned deep priors. ACM Trans. Graph. 36(4): 119:1-119:11 (2017) - [c3]Chelsea Finn, Tianhe Yu, Tianhao Zhang, Pieter Abbeel, Sergey Levine:
One-Shot Visual Imitation Learning via Meta-Learning. CoRL 2017: 357-368 - [c2]Chelsea Finn, Tianhe Yu, Justin Fu, Pieter Abbeel, Sergey Levine:
Generalizing Skills with Semi-Supervised Reinforcement Learning. ICLR (Poster) 2017 - [i3]Richard Zhang, Jun-Yan Zhu, Phillip Isola, Xinyang Geng, Angela S. Lin, Tianhe Yu, Alexei A. Efros:
Real-Time User-Guided Image Colorization with Learned Deep Priors. CoRR abs/1705.02999 (2017) - [i2]Chelsea Finn, Tianhe Yu, Tianhao Zhang, Pieter Abbeel, Sergey Levine:
One-Shot Visual Imitation Learning via Meta-Learning. CoRR abs/1709.04905 (2017) - 2016
- [c1]Tianhe Yu, Yongjin Zhang, Jingmin Dai:
Calibration Method to the Temperature Measuring System Based on Color CCD Camera. IGTA 2016: 44-50 - [i1]Chelsea Finn, Tianhe Yu, Justin Fu, Pieter Abbeel, Sergey Levine:
Generalizing Skills with Semi-Supervised Reinforcement Learning. CoRR abs/1612.00429 (2016)
Coauthor Index
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last updated on 2023-09-28 02:52 CEST by the dblp team
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