I aim to build machine intelligence that can actively learn from interactions, with the ability of reasoning, planning, and generalization.
My research focuses on environment understanding for decision-making, which involves reinforcement learning, computer vision, and embodied agents.
I will spend this summer in Facebook AI Research (FAIR) as a research intern.
Contact: fliu [at] eng.ucsd.edu. My CV can be found here.
University of California San DiegoSep. 2018 - Mar. 2020
M.S. in Computer Science
Peking UniversitySep. 2014 - Jul. 2018
B.S. in Computer Science (Honored Degree)
SAPIEN: a SimulAted Part-based Interactive ENvironment
Fanbo Xiang, Yuzhe Qin, Kaichun Mo, Yikuan Xia, Hao Zhu, Fangchen Liu, Minghua Liu, Hanxiao Jiang, Yifu Yuan, Li Yi, He Wang, Angel Chang, Leonidas Guibas, Hao Su.
CVPR 2020 (oral) We constructed a PhysX-based simulation environment using PartNet-Mobility dataset. My work focused on reinforcement learning-based robot manipulation in the simulator. In this part, ResNet-18/PointNet++ is used to encode the object mobility given RGB-D/point cloud of the interactive scene, which serve as the state representation for the RL agents.
State Alignment-based Imitation Learning
Fangchen Liu, Zhan Ling, Tongzhou Mu, Hao Su
Paper / Code Coming SoonWe propose a robust state-based imitation learning method and unify global and local alignment constraints into a novel RL objective. It achieves the SOTA on traditional imitation learning tasks, and can also imitate policies from heterogeneous experts.
Mapping State Space using Landmarks for Universal Goal Reaching
Fangchen Liu*, Zhiao Huang*, Hao Su
Adversarial Defense by Stratified Convolutional Sparse Coding
Bo Sun, Nian-Tsuan Tsai, Fangchen Liu, Ronald Yu, Hao Su
Revisiting the Master-Slave Architecture in Multi-Agent Deep Reinforcement Learning
Xiangyu Kong, Fangchen Liu*, Bo Xin*, Yizhou Wang