Calibration Method to the Temperature Measuring System Based on Color CCD Camera. - Tianhe Yu; Yongjin Zhang; Jingmin Dai (IGTA, 2016)

One-Shot Visual Imitation Learning via Meta-Learning. - Chelsea Finn; Tianhe Yu; Tianhao Zhang 001; Pieter Abbeel; Sergey Levine (CoRL, 2017)

Generalizing Skills with Semi-Supervised Reinforcement Learning. - Chelsea Finn; Tianhe Yu; Justin Fu; Pieter Abbeel; Sergey Levine (ICLR (Poster), 2017)

Real-time user-guided image colorization with learned deep priors. - Richard Zhang 001; Jun-Yan Zhu; Phillip Isola; Xinyang Geng; Angela S. Lin; Tianhe Yu; Alexei A. Efros (ACM Trans. Graph., 2017)

SMEM++: A Pipelined and Time-Multiplexed SMEM Seeding Accelerator for DNA Sequencing. - Jason Cong; Licheng Guo; Po-Tsang Huang; Peng Wei 004; Tianhe Yu (FCCM, 2018)

SMEM++: A Pipelined and Time-Multiplexed SMEM Seeding Accelerator for Genome Sequencing. - Jason Cong; Licheng Guo; Po-Tsang Huang; Peng Wei 004; Tianhe Yu (FPL, 2018)

One-Shot Imitation from Observing Humans via Domain-Adaptive Meta-Learning. - Tianhe Yu; Chelsea Finn; Annie Xie; Sudeep Dasari; Tianhao Zhang 001; Pieter Abbeel; Sergey Levine (ICLR (Workshop), 2018)

One-Shot Hierarchical Imitation Learning of Compound Visuomotor Tasks. - Tianhe Yu; Pieter Abbeel; Sergey Levine; Chelsea Finn (CoRR, 2018)

SMEM++: A Pipelined and Time-Multiplexed SMEM Seeding Accelerator for Genome Sequencing. - Tianhe Yu (2018)

Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning. - Tianhe Yu; Deirdre Quillen; Zhanpeng He; Ryan Julian; Karol Hausman; Chelsea Finn; Sergey Levine (CoRL, 2019)

One-Shot Composition of Vision-Based Skills from Demonstration. - Tianhe Yu; Pieter Abbeel; Sergey Levine; Chelsea Finn (IROS, 2019)

Meta-Inverse Reinforcement Learning with Probabilistic Context Variables. - Lantao Yu; Tianhe Yu; Chelsea Finn; Stefano Ermon (NeurIPS, 2019)

Unsupervised Visuomotor Control through Distributional Planning Networks. - Tianhe Yu; Gleb Shevchuk; Dorsa Sadigh; Chelsea Finn (Robotics: Science and Systems, 2019)

On the Expressivity of Neural Networks for Deep Reinforcement Learning. - Kefan Dong; Yuping Luo; Tianhe Yu; Chelsea Finn; Tengyu Ma 001 (ICML, 2020)

Gradient Surgery for Multi-Task Learning. - Tianhe Yu; Saurabh Kumar 004; Abhishek Gupta 004; Sergey Levine; Karol Hausman; Chelsea Finn (NeurIPS, 2020)

MOPO: Model-based Offline Policy Optimization. - Tianhe Yu; Garrett Thomas; Lantao Yu; Stefano Ermon; James Y. Zou; Sergey Levine; Chelsea Finn; Tengyu Ma 001 (NeurIPS, 2020)

Measuring and Harnessing Transference in Multi-Task Learning. - Christopher Fifty; Ehsan Amid; Zhe Zhao 001; Tianhe Yu; Rohan Anil; Chelsea Finn (CoRR, 2020)

Variable-Shot Adaptation for Online Meta-Learning. - Tianhe Yu; Xinyang Geng; Chelsea Finn; Sergey Levine (CoRR, 2020)

Offline Reinforcement Learning from Images with Latent Space Models. - Rafael Rafailov; Tianhe Yu; Aravind Rajeswaran; Chelsea Finn (L4DC, 2021)

Efficiently Identifying Task Groupings for Multi-Task Learning. - Chris Fifty; Ehsan Amid; Zhe Zhao 001; Tianhe Yu; Rohan Anil; Chelsea Finn (NeurIPS, 2021)

Visual Adversarial Imitation Learning using Variational Models. - Rafael Rafailov; Tianhe Yu; Aravind Rajeswaran; Chelsea Finn (NeurIPS, 2021)

Conservative Data Sharing for Multi-Task Offline Reinforcement Learning. - Tianhe Yu; Aviral Kumar; Yevgen Chebotar; Karol Hausman; Sergey Levine; Chelsea Finn (NeurIPS, 2021)

COMBO: Conservative Offline Model-Based Policy Optimization. - Tianhe Yu; Aviral Kumar; Rafael Rafailov; Aravind Rajeswaran; Sergey Levine; Chelsea Finn (NeurIPS, 2021)

Image Enhancement Algorithm Based on Image Spatial Domain Segmentation. - Tianhe Yu; Ming Zhu (Comput. Informatics, 2021)

Adaptive superpixel-based multi-object pedestrian recognition. - Tianhe Yu; Chengdong Wang; Xiao Liu; Ming Zhu (Mach. Vis. Appl., 2021)

Offline Reinforcement Learning at Multiple Frequencies. - Kaylee Burns; Tianhe Yu; Chelsea Finn; Karol Hausman (CoRL, 2022)

How to Leverage Unlabeled Data in Offline Reinforcement Learning. - Tianhe Yu; Aviral Kumar; Yevgen Chebotar; Karol Hausman; Chelsea Finn; Sergey Levine (ICML, 2022)

LAPO: Latent-Variable Advantage-Weighted Policy Optimization for Offline Reinforcement Learning. - Xi Chen; Ali Ghadirzadeh; Tianhe Yu; Jianhao Wang; Alex Yuan Gao; Wenzhe Li; Liang Bin; Chelsea Finn; Chongjie Zhang (NeurIPS, 2022)

Latent-Variable Advantage-Weighted Policy Optimization for Offline RL. - Xi Chen 051; Ali Ghadirzadeh; Tianhe Yu; Yuan Gao 024; Jianhao Wang; Wenzhe Li; Bin Liang 001; Chelsea Finn; Chongjie Zhang (CoRR, 2022)

Single image dehazing based on multi-scale segmentation and deep learning. - Tianhe Yu; Ming Zhu; Haiming Chen (Mach. Vis. Appl., 2022)

Offline Imitation Learning with Suboptimal Demonstrations via Relaxed Distribution Matching. - Lantao Yu; Tianhe Yu; Jiaming Song; Willie Neiswanger; Stefano Ermon (AAAI, 2023)

Q-Transformer: Scalable Offline Reinforcement Learning via Autoregressive Q-Functions. - Yevgen Chebotar; Quan Vuong; Karol Hausman; Fei Xia 002; Yao Lu 006; Alex Irpan; Aviral Kumar; Tianhe Yu; Alexander Herzog; Karl Pertsch; Keerthana Gopalakrishnan; Julian Ibarz; et al. (CoRL, 2023)

RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control. - Brianna Zitkovich; Tianhe Yu; Sichun Xu; Peng Xu 010; Ted Xiao; Fei Xia 002; Jialin Wu; Paul Wohlhart; Stefan Welker; Ayzaan Wahid; Quan Vuong; Vincent Vanhoucke; et al. (CoRL, 2023)

PaLM-E: An Embodied Multimodal Language Model. - Danny Driess; Fei Xia 002; Mehdi S. M. Sajjadi; Corey Lynch; Aakanksha Chowdhery; Brian Ichter; Ayzaan Wahid; Jonathan Tompson; Quan Vuong; Tianhe Yu; Wenlong Huang; Yevgen Chebotar; et al. (ICML, 2023)

Train Offline, Test Online: A Real Robot Learning Benchmark. - 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 001 (ICRA, 2023)

Contrastive Example-Based Control. - Kyle Beltran Hatch; Benjamin Eysenbach; Rafael Rafailov; Tianhe Yu; Ruslan Salakhutdinov; Sergey Levine; Chelsea Finn (L4DC, 2023)

RT-1: Robotics Transformer for Real-World Control at Scale. - Anthony Brohan; Noah Brown; Justice Carbajal; Yevgen Chebotar; Joseph Dabis; Chelsea Finn; Keerthana Gopalakrishnan; Karol Hausman; Alexander Herzog; Jasmine Hsu; Julian Ibarz; Brian Ichter; et al. (Robotics: Science and Systems, 2023)

Scaling Robot Learning with Semantically Imagined Experience. - Tianhe Yu; Ted Xiao; Jonathan Tompson; Austin Stone; Su Wang; Anthony Brohan; Jaspiar Singh; Clayton Tan; Dee M; Jodilyn Peralta; Karol Hausman; Brian Ichter; et al. (Robotics: Science and Systems, 2023)

What Makes Pre-Trained Visual Representations Successful for Robust Manipulation? - Kaylee Burns; Zach Witzel; Jubayer Ibn Hamid; Tianhe Yu; Chelsea Finn; Karol Hausman (CoRL, 2024)

RT-Sketch: Goal-Conditioned Imitation Learning from Hand-Drawn Sketches. - Priya Sundaresan; Quan Vuong; Jiayuan Gu; Peng Xu 010; Ted Xiao; Sean Kirmani; Tianhe Yu; Michael Stark 003; Ajinkya Jain; Karol Hausman; Dorsa Sadigh; Jeannette Bohg; et al. (CoRL, 2024)

Video Language Planning. - Yilun Du; Sherry Yang 001; Pete Florence; Fei Xia 002; Ayzaan Wahid; Brian Ichter; Pierre Sermanet; Tianhe Yu; Pieter Abbeel; Joshua B. Tenenbaum; Leslie Pack Kaelbling; Andy Zeng 001; et al. (ICLR, 2024)

Open X-Embodiment: Robotic Learning Datasets and RT-X Models : Open X-Embodiment Collaboration. - Abby O'Neill; Abdul Rehman; Abhiram Maddukuri; Abhishek Gupta 004; Abhishek Padalkar; Abraham Lee; Acorn Pooley; Agrim Gupta; Ajay Mandlekar; Ajinkya Jain; Albert Tung; Alex Bewley; et al. (ICRA, 2024)

RRM: Robust Reward Model Training Mitigates Reward Hacking. - Tianqi Liu 002; Wei Xiong 015; Jie Ren 006; Lichang Chen; Junru Wu; Rishabh Joshi; Yang Gao; Jiaming Shen; Zhen Qin 001; Tianhe Yu; Daniel Sohn; Anastasiia Makarova; et al. (CoRR, 2024)

ReARTSim: an ReRAM ARray Transient Simulator with GPU optimized runtime acceleration. - Yu Sui; Tianhe Yu; Shiming Song (Neuromorph. Comput. Eng., 2024)

RRM: Robust Reward Model Training Mitigates Reward Hacking. - Tianqi Liu 002; Wei Xiong 015; Jie Ren 006; Lichang Chen; Junru Wu; Rishabh Joshi; Yang Gao; Jiaming Shen; Zhen Qin 001; Tianhe Yu; Daniel Sohn; Anastasia Makarova; et al. (ICLR, 2025)

Meta-World+: An Improved, Standardized, RL Benchmark. - Reginald McLean; Evangelos Chatzaroulas; Luc McCutcheon; Frank Röder; Tianhe Yu; Zhanpeng He; K. R. Zentner; Ryan Julian; Jordan K. Terry; Isaac Woungang; Nariman Farsad; Pablo Samuel Castro (CoRR, 2025)