Sheng-Hua Wan @ LAMDA, NJU-AI

万盛华
Sheng-Hua Wan
Ph.D. candidate, LAMDA Group
School of Artificial Intelligence
Nanjing University, Nanjing 210023, China

Email: wansh [at] lamda.nju.edu.cn
Github: https://github.com/yixiaoshenghua

• Sheng-hua Wan, Haihang Sun, De-chuan Zhan. MOSER: Learning Sensory Policy for Task-specific Viewpoint via View-conditional World Model. [Paper] [Code]

Existing visual RL algorithms mostly rely on a single observation from a well-designed fixed camera that requires human knowledge. Recent studies learn from different viewpoints with multiple fixed cameras, but this incurs high computation and storage costs and does not guarantee the coverage of the optimal viewpoint. To address these issues, we propose the View-conditional Markov Decision Process (VMDP) assumption and develop a new method, the MOdel-based SEnsor controlleR (MOSER), based on VMDP. MOSER jointly learns a view-conditional world model (VWM) to simulate the environment, a sensory policy to control the camera, and a motor policy to complete tasks. We design intrinsic rewards from the VWM without additional modules to guide the sensory policy to adjust the camera parameters.

• Sheng-hua Wan, Yu-cen Wang, Ming-hao Shao, Ru-ying Chen, De-chuan Zhan. SeMAIL: Eliminating Distractors in Visual Imitation vis Separated Models. In Proceedings of the 40th International Conference on Machine Learning (ICML-2023), 2023. [Paper] [Code]

Existing Model-based imitation learning algorithms are highly deceptive by task-irrelevant information, especially moving distractors in videos. To tackle this problem, we propose a new algorithm - named Separated Model-based Adversarial Imitation Learning (SeMAIL) - decoupling the environment dynamics into two parts by task-relevant dependency, which is determined by agent actions, and training separately.

• Wen-ye Wang, Sheng-hua Wan, Peng-feng Xiao, Xue-liang Zhang. A Novel Multi-Training Method for Time-Series Urban Green Cover Recognition From Multitemporal Remote Sensing Images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (2022), 15, 9531-9544. [Paper] [Code] (Completed at undergraduate)

We designed a general multitemporal framework to extract urban green cover using multi-training, a novel semi-supervised learning method for land cover classification on multitemporal remote sensing images.