Yuanpei Chen | 陈源培

I am a fourth-year undergraduate student at South China University of Technology, working with Prof. Yaodong Yang at Peking University. I also had the privilege of working closely with Prof. Xiaolong Wang at UCSD, Prof. Chenguang Yang at SCUT and Prof. Hao Dong at Peking University. In 2023, I am fortunately working as a visiting researcher at Stanford University, advised by Prof. Karen Liu and Prof. Fei-Fei Li.

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I'm interested in robotics, dexterous manipulation, reinforcement learning, and sim-to-real transfer. My goal is to let the robot perform various cool behaviors in the real world as in the simulation.

Representative publication
Sequential Dexterity: Chaining Dexterous Policies for Long-Horizon Manipulation
Yuanpei Chen*, Chen Wang*, Li Fei-Fei, C. Karen Liu,
CoRL, 2023, Accepted
Project Page / ArXiv / Code / Video

We present Sequential Dexterity, a general system based on reinforcement learning (RL) that chains multiple dexterous policies for achieving long-horizon task goals.

Dynamic Handover: Throw and Catch with Bimanual Hands
Binghao Huang*, Yuanpei Chen*, Tianyu Wang, Yuzhe Qin, Yaodong Yang, Nikolay Atanasov, Xiaolong Wang
CoRL, 2023, Accepted
Project Page / ArXiv / Code

We design a system with two multi-finger hands attached to robot arms to solve the dynamic handover problem. We train our system using Multi-Agent Reinforcement Learning in simulation and perform Sim2Real transfer to deploy on the real robots.

Towards Human-Level Bimanual Dexterous Manipulation with Reinforcement Learning
Yuanpei Chen, Tianhao Wu, Shengjie Wang, Xidong Feng, Jiechuang Jiang, Stephen Marcus McAleer, Hao Dong, Zongqing Lu, Song-chun Zhu, Yaodong Yang
Journal Version: T-PAMI, 2023, Accepted
Conference Version: NeurIPS, 2022, Accepted
Project Page / ArXiv / Code

We propose a bimanual dexterous manipulation benchmark (Bi-DexHands) according to literature from cognitive science for comprehensive reinforcement learning research.

Collaborative publication
ReDMan: Reliable Dexterous Manipulation with Safe Reinforcement Learning
Yiran Geng*, Jiaming Ji*, Yuanpei Chen*, Haoran Geng, Fangwei Zhong, Yaodong Yang
Paper / Code
Machine Learning (Journal), 2023, Accepted

We introduce ReDMan, an open-source simulation platform that provides a standardized implementation of safe RL algorithms for Reliable Dexterous Manipulation.

Learning a Universal Human Prior for Dexterous Manipulation from Human Preference
Zihan Ding, Yuanpei Chen, Allen Z. Ren, Shixiang Shane Gu, Hao Dong, Chi Jin
Arxiv / Project Page / Provide Your Preference / Code (Coming soon)
RSS Workshop on Learning Dexterous Manipulation, 2023, Accepted

We propose a framework to learn a universal human prior using direct human preference feedback over videos, for efficiently tuning the RL policy on 20 dual-hand robot manipulation tasks in simulation, without a single human demonstration

MyoChallenge: Learning contact-rich manipulation using a musculoskeletal hand
Yiran Geng, Boshi An, Yifan Zhong, Jiaming Ji, Yuanpei Chen, Hao Dong, Yaodong Yang
Challenge Page / Code / Slides / Talk / Award / Media (BIGAI) / Media (CFCS) / Media (PKU-EECS) / Media (PKU-IAI) / Media (PKU) / Media (China Youth Daily)
First Place in NeurIPS 2022 Challenge Track (1st in 340 submissions from 40 teams)
PMLR, 2023, Accepted

Reconfiguring a die to match desired goal orientations. This task require delicate coordination of various muscles to manipulate the die without dropping it.

Safe Multi-Agent Reinforcement Learning for Multi-Robot Control
Shangding Gu*, Jakub Grudzien Kuba*, Yuanpei Chen, Yali Du, Long Yang, Alois Knoll, Yaodong Yang
Journal of Artificial Intelligence (AIJ), 2022, Accepted
project Page / Code

We investigate safe MARL for multi-robot control on cooperative tasks, in which each individual robot has to not only meet its own safety constraints while maximising their reward, but also consider those of others to guarantee safe team behaviours.

End-to-End Affordance Learning for Robotic Manipulation
Yiran Geng*, Boshi An*, Haoran Geng, Yuanpei Chen, Yaodong Yang, Hao Dong
(*equal contribution)
ICRA, 2022, Accepted
Project Page / ArXiv

In this study, we take advantage of visual affordance by using the contact information generated during the RL training process to predict contact maps of interest.

Zero-Shot Sim-to-Real Transfer of Reinforcement Learning Framework for Robotics Manipulation with Demonstration and Force Feedback
Yuanpei Chen, Chao Zeng, Zhiping Wang, Peng Lu, Chenguang Yang
IEEE-ARM, 2022, Outstanding Paper Selected for Robotica Journal
Project Page / Paper / Code

We propose Simulation Twin (SimTwin) : a deep reinforcement learning framework that can help directly transfer the model from simulation to reality without any real-world training.

Stanford University, CA
2022.10 - Present

Visiting Research Student
Advisor: Prof. Karen Liu and Prof. Fei-Fei Li
Peking University, China
2022.03 - 2023.07

Visiting Research Student
Advisor: Prof. Yaodong Yang
South China University of Technology, China
2019.09 - 2023.07

Advisor: Prof. Chenguang Yang

I was quite into competitive robot🤖 and used to compete in RoboMaster🏅 and ICRA 2021 AI Challenge.

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Last updated: Nov 6, 2021