Jianshu Hu

I'm a PhD student at UM-SJTU Joint Institute in Shanghai, working on sample-efficient and generalizable robot learning. I am currently advised by Yutong Ban and Paul Weng. Before my PhD study, I finished my master degree in Upenn GRASP, advised by Michael Posa.

Email / Scholar / Github

Research

I'm interested in reinforcement learning and robot manipulation. My current research focuses on improving sample-efficiency and generalization ability of robot learning algorithms by exploiting data augmentation, leveraging pre-trained models, learning a dynamics model/world model. Some papers are highlighted.

	State-Novelty Guided Action Persistence in Deep Reinforcement Learning Jianshu Hu, Paul Weng, Yutong Ban, Machine Learning Journal arXiv In this paper, we propose a novel method to dynamically adjust the action persistence based on the current exploration status of the state space.
	Revisiting Data Augmentation in Deep Reinforcement Learning Jianshu Hu, Yunpeng Jiang, Paul Weng, ICLR, 2024 arXiv We make recommendations on how to exploit data augmentation in image-based DRL in a more principled way. And we include a novel regularization term called tangent prop in RL training.
	Solving Complex Manipulation Tasks with Model-Assisted Model-Free Reinforcement Learning Jianshu Hu, Paul Weng, CoRL, 2022 paper link A novel deep reinforcement learning approach for improving the sample efﬁciency of a model-free actor-critic method by using a learned model to encourage exploration.
	Beyond Inverted Pendulums: Task-Optimal Simple Models of Legged Locomotion Yu-Ming Chen, Jianshu Hu, Michael Posa,, T-RO arXiv We propose a model optimization algorithm that automatically synthesizes reduced-order models.

Miscellanea

Reviewer, ICLR 2024
Reviewer, TNNLS

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