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.

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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.

We propose Time Reversal symmetry enhanced Deep Reinforcement Learning (TR-DRL), a framework that combines trajectory reversal augmentation and time reversal guided reward shaping to efficiently solve temporally symmetric tasks.

Aiming to extend the successes in solving manipulation tasks to the domain of surgical robotics, we propose a diffusion-based policy learning framework, called Diffusion Stabilizer Policy, which enables training with imperfect or even failed trajectories.

In this paper, we propose a novel method to dynamically adjust the action persistence based on the current exploration status of the state space.

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.

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.

We propose a model optimization algorithm that automatically synthesizes reduced-order models.

Miscellanea

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