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Email: zhanpeng at cs.columbia.edu

Zhanpeng He

I am a Ph.D candidate in computer science at Columbia University. My research focuses on robotics, computer vision and reinforcement learning. I am privileged to be co-advised by Professor Matei Ciocarlie and Professor Shuran Song. I am also a member of the Robotic Manipulation and Mobility (ROAM) Lab and the Columbia Artificial Intelligence and Robotics (CAIR) Lab.

Before joining CU, I recieved a master's degree from University of Southern California, where I worked as a research assistant at the Robotic Embedded System Laboratory (RESL) and advised by Professor Gaurav Sukhatme and Professor Stefan Schaal. Before joining USC, I received a Bachelor of Science degree in Computer Science from the Rutgers University.

I am always happy to mentor highly-motivated undergraduate/master students. If you are at Columbia (preferred) and want to discuss project ideas/research opportunities, please feel free to shoot me an email. Currently, I have the luck to work with: Toby Kreiman, Siddharth Singi, Rohan James.

Updates

Publications (* - indicates equal contribution)

MORPH: Design Co-optimization with Reinforcement Learning via a Differentiable Hardware Model Proxy

Zhanpeng He, Matei Ciocarlie
International Conference on Robotics and Automation (ICRA) 2024
Webpage  /   Paper

Decision Making for Human-in-the-loop Robotic Agents via Uncertainty-Aware Reinforcement Learning

Siddharth Singi*, Zhanpeng He*, Alvin Pan, Sandip Patel, Gunnar A. Sigurdsson, Robinson Piramuthu, Shuran Song, Matei Ciocarlie
International Conference on Robotics and Automation (ICRA) 2024
Webpage  /   Paper

Pick2Place: Task-aware 6DoF Grasp Estimation via Object-Centric Perspective Affordance

Zhanpeng He, Nikhil Chavan-Dafle, Jinwook Huh, Shuran Song, Volkan Isler
International Conference on Robotics and Automation (ICRA) 2023
Paper

Discovering Synergies for Robot Manipulation with Multi-Task Reinforcement Learning

Zhanpeng He, and Matei Ciocarlie
International Conference on Robotics and Automation (ICRA) 2022
Webpage  /   Paper   /   Codes

UMPNet: Universal Manipulation Policy Network for Articulated Objects

Zhenjia Xu, Zhanpeng He, and Shuran Song
Robotics and Automation Letters (RA-L)
International Conference on Robotics and Automation (ICRA) 2022
Webpage  /   Paper   /   Codes

Hardware as Policy: Mechanical and ComputationalCo-Optimization using Deep Reinforcement Learning

Tianjian Chen*, Zhanpeng He*, and Matei Ciocarlie
Science Robotics
Conference on Robot Learning (CoRL) 2020
Webpage  /   Paper  /   Science Robotics Article

Learning 3D Dynamic Scene Representations for Robot Manipulation

Zhenjia Xu*, Zhanpeng He*, Jiajun Wu, and Shuran Song
Conference on Robot Learning (CoRL) 2020
Webpage  /   Paper  /   Codes

SQUIRL: Robust and Efficient Learning from Video Demonstration of Long-Horizon Robotic Manipulation Tasks

Bohan Wu, Feng Xu, Zhanpeng He, Abhi Gupta, and Peter K. Allen
International Conference on Intelligent Robots and Systems (IROS) 2020
Webpage  /   Paper  /   Video

Meta-World: A Benchmark and Evaluation for Multi-Task and Meta- Reinforcement Learning

Tianhe Yu*, Deirdre Quillen*, Zhanpeng He*, Ryan C Julian, Karol Hausman, Sergey Levine, and Chelsea Finn
Conference on Robot Learning (CoRL) 2019
Webpage  /   Paper  /   Codes

Simulator Predictive Control: Using Learned Task Representations and MPC for Zero-Shot Generalization and Sequencing

Zhanpeng He*, Ryan C Julian*, Eric Heiden, Hejia Zhang, Stefan Schaal, Joseph Lim, Gaurav S Sukhatme, and Karol Hausman
Neural Information Processing Systems (NIPS) 2018
Paper  /   Codes

Scaling Simulation-to-real Transfer by Learning Composable Robot Skills

Ryan C Julian*, Eric Heiden*, Zhanpeng He, Hejia Zhang, Stefan Schaal, Joseph Lim, Gaurav S Sukhatme, and Karol Hausman
The International Journal of Robotics Research (IJRR)
International Symposium on Experimental Robotics (ISER) 2018
Paper  /   Codes  /   Video  /   Journal version

Teaching

Softwares

I was a member of rlworkgroup and took part in development of several robot-learning-related open-source projects.

  • Garage: A toolkit for reproducible reinforcement learning research.
  • Dowel: A little logger for machine learning research.
  • Meta-World: A collection of environments for benchmarking meta-learning and multi-task reinforcement learning algorithms.