Yi Zhao

I am a PhD candidate at Robot Learning Group, Aalto University, advised by Joni Pajarinen and Juho Kannala. My research interests are reinforcement learning and robot learning. I received my MSc degree from Aalto University, Finland and BEng degree from Huazhong University of Science and Technology, China. I am currently visiting at Max Planck Institute for Intelligent Systems, working with Dieter Büchler and Bernhard Schölkopf.

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Experience
  • Aalto University, Finland
    Aalto University, Finland
    Doctoral Candidate
    Feb. 2021 - now
  • Max Planck Institute for Intelligent Systems, Germany
    Max Planck Institute for Intelligent Systems, Germany
    Research Visit
    Feb. 2024 - now
  • Aalto University, Finland
    Aalto University, Finland
    Master of Science
    2020
  • Huazhong University of Science and Technology, China
    Huazhong University of Science and Technology, China
    Bachelor of Engineering
    2017
Publications (view all )
RP1M: A Large-Scale Motion Dataset for Piano Playing with Bi-Manual Dexterous Robot Hands
RP1M: A Large-Scale Motion Dataset for Piano Playing with Bi-Manual Dexterous Robot Hands

Yi Zhao*, Le Chen*, Jan Schneider, Quankai Gao, Juho Kannala, Bernhard Schölkopf, Joni Pajarinen, Dieter Büchler (* equal contribution)

Conference on Robot Learning (CoRL) 2024

[Paper] [Website]

RP1M: A Large-Scale Motion Dataset for Piano Playing with Bi-Manual Dexterous Robot Hands
RP1M: A Large-Scale Motion Dataset for Piano Playing with Bi-Manual Dexterous Robot Hands

Yi Zhao*, Le Chen*, Jan Schneider, Quankai Gao, Juho Kannala, Bernhard Schölkopf, Joni Pajarinen, Dieter Büchler (* equal contribution)

Conference on Robot Learning (CoRL) 2024

[Paper] [Website]

Leveraging Unlabeled Offline Data via Model-based Active Semi-supervised Reinforcement Learning

Yi Zhao, Aidan Scannell, Tianyu Cui, Le Chen, Dieter Büchler, Arno Solin, Juho Kannala, Joni Pajarinen

Under review, preprint to appear soon 2024

Leveraging Unlabeled Offline Data via Model-based Active Semi-supervised Reinforcement Learning
Leveraging Unlabeled Offline Data via Model-based Active Semi-supervised Reinforcement Learning

Yi Zhao, Aidan Scannell, Tianyu Cui, Le Chen, Dieter Büchler, Arno Solin, Juho Kannala, Joni Pajarinen

Under review, preprint to appear soon 2024

Bi-Level Motion Imitation for Humanoid Robots
Bi-Level Motion Imitation for Humanoid Robots

Wenshuai Zhao, Yi Zhao, Joni Pajarinen, Michael Muehlebach

Conference on Robot Learning (CoRL) 2024

Bi-Level Motion Imitation for Humanoid Robots
Bi-Level Motion Imitation for Humanoid Robots

Wenshuai Zhao, Yi Zhao, Joni Pajarinen, Michael Muehlebach

Conference on Robot Learning (CoRL) 2024

iQRL--Implicitly Quantized Representations for Sample-efficient Reinforcement Learning
iQRL--Implicitly Quantized Representations for Sample-efficient Reinforcement Learning

Aidan Scannell, Kalle Kujanpää, Yi Zhao, Mohammadreza Nakhaei, Arno Solin, Joni Pajarinen

International Conference on Machine Learning, Workshop (ICML Workshop) 2024

[Paper]

iQRL--Implicitly Quantized Representations for Sample-efficient Reinforcement Learning
iQRL--Implicitly Quantized Representations for Sample-efficient Reinforcement Learning

Aidan Scannell, Kalle Kujanpää, Yi Zhao, Mohammadreza Nakhaei, Arno Solin, Joni Pajarinen

International Conference on Machine Learning, Workshop (ICML Workshop) 2024

[Paper]

Optimistic Multi-Agent Policy Gradient
Optimistic Multi-Agent Policy Gradient

Wenshuai Zhao, Yi Zhao, Zhiyuan Li, Juho Kannala, Joni Pajarinen

International Conference on Machine Learning (ICML) 2024

[Paper] [Code] [Website]

Optimistic Multi-Agent Policy Gradient
Optimistic Multi-Agent Policy Gradient

Wenshuai Zhao, Yi Zhao, Zhiyuan Li, Juho Kannala, Joni Pajarinen

International Conference on Machine Learning (ICML) 2024

[Paper] [Code] [Website]

Hscnet++: Hierarchical Scene Coordinate Classification and Regression for Visual Localization with Transformer
Hscnet++: Hierarchical Scene Coordinate Classification and Regression for Visual Localization with Transformer

Shuzhe Wang, Zakaria Laskar, Iaroslav Melekhov, Xiaotian Li, Yi Zhao, Giorgos Tolias, Juho Kannala

International Journal of Computer Vision (IJCV) 2024

[Paper]

Hscnet++: Hierarchical Scene Coordinate Classification and Regression for Visual Localization with Transformer
Hscnet++: Hierarchical Scene Coordinate Classification and Regression for Visual Localization with Transformer

Shuzhe Wang, Zakaria Laskar, Iaroslav Melekhov, Xiaotian Li, Yi Zhao, Giorgos Tolias, Juho Kannala

International Journal of Computer Vision (IJCV) 2024

[Paper]

Continuous Monte Carlo Graph Search
Continuous Monte Carlo Graph Search

Kalle Kujanpää*, Amin Babadi*, Yi Zhao, Juho Kannala, Alexander Ilin, Joni Pajarinen (* equal contribution)

International Conference on Autonomous Agents and Multiagent Systems (AAMAS) 2023

[Paper]

Continuous Monte Carlo Graph Search
Continuous Monte Carlo Graph Search

Kalle Kujanpää*, Amin Babadi*, Yi Zhao, Juho Kannala, Alexander Ilin, Joni Pajarinen (* equal contribution)

International Conference on Autonomous Agents and Multiagent Systems (AAMAS) 2023

[Paper]

Simplified Temporal Consistency Reinforcement Learning
Simplified Temporal Consistency Reinforcement Learning

Yi Zhao, Wenshuai Zhao, Rinu Boney, Juho Kannala, Joni Pajarinen

International Conference on Machine Learning (ICML) 2023

[Paper] [Code]

Simplified Temporal Consistency Reinforcement Learning
Simplified Temporal Consistency Reinforcement Learning

Yi Zhao, Wenshuai Zhao, Rinu Boney, Juho Kannala, Joni Pajarinen

International Conference on Machine Learning (ICML) 2023

[Paper] [Code]

Adaptive Behavior Cloning Regularization for Stable Offline-to-Online Reinforcement Learning
Adaptive Behavior Cloning Regularization for Stable Offline-to-Online Reinforcement Learning

Yi Zhao*, Rinu Boney*, Alexander Ilin, Juho Kannala, Joni Pajarinen (* equal contribution)

European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning 2022

[Paper] [Code]

Adaptive Behavior Cloning Regularization for Stable Offline-to-Online Reinforcement Learning
Adaptive Behavior Cloning Regularization for Stable Offline-to-Online Reinforcement Learning

Yi Zhao*, Rinu Boney*, Alexander Ilin, Juho Kannala, Joni Pajarinen (* equal contribution)

European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning 2022

[Paper] [Code]

Learning to drive (l2d) as a low-cost benchmark for real-world reinforcement learning
Learning to drive (l2d) as a low-cost benchmark for real-world reinforcement learning

Ari Viitala*, Rinu Boney*, Yi Zhao, Alexander Ilin, Juho Kannala (* equal contribution)

International Conference on Advanced Robotics 2021

[Paper] [Website]

Learning to drive (l2d) as a low-cost benchmark for real-world reinforcement learning
Learning to drive (l2d) as a low-cost benchmark for real-world reinforcement learning

Ari Viitala*, Rinu Boney*, Yi Zhao, Alexander Ilin, Juho Kannala (* equal contribution)

International Conference on Advanced Robotics 2021

[Paper] [Website]

All publications