Gilbert Feng

I am interested in modeling the world using mathematics, engineering, and computer science. Starting August 2023, I work on quantitative trading at Jane Street.

I previously graduated with a B.S. in Electrical Engineering and Computer Sciences from UC Berkeley, where I was involved in research advised by Professor Sergey Levine at the Robotic AI & Learning Lab.

gilbertfeng @ berkeley.edu / GitHub / Scholar / Coursework

Research

	Open X-Embodiment: Robotic Learning Datasets and RT-X Models Open X-Embodiment Collaboration IEEE International Conference on Robotics and Automation (ICRA), 2024 (Best Conference Paper Award) paper / code / website /
	Multi-Stage Cable Routing Through Hierarchical Imitation Learning Jianlan Luo, Charles Xu, Xinyang Geng, Gilbert Feng, Kuan Fang, Liam Tan, Stefan Schaal, Sergey Levine IEEE Transactions on Robotics (T-RO), 2024 paper / video / code / website /
	GenLoco: Generalized Locomotion Controllers for Quadrupedal Robots Gilbert Feng, Hongbo Zhang, Zhongyu Li, Xue Bin Peng, Bhuvan Basireddy, Linzhu Yue, Zhitao Song, Lizhi Yang, Yunhui Liu, Koushil Sreenath, Sergey Levine Conference on Robot Learning (CoRL), 2022 paper / video / code /
	Predictive Signatures for Lung Adenocarcinoma Prognostic Trajectory by Multiomics Data Integration and Ensemble Learning Hayan Lee, Gilbert Feng, Ed Esplin, Michael Snyder International Symposium on Mathematical and Computational Oncology (ISMCO), 2021 (Best Paper Award) paper /