Shengjie Wang | 王圣杰

I am a PhD student in Computer Science at Institute for Interdisciplinary Information Sciences (IIIS), Tsinghua University , working with Prof. Yang Gao. Previously, I was a Master's student in Department of Automation at Tsinghua University , advised by Prof. Tao Zhang(IET Fellow, Head of Department). I completed my Bachelor's in Robotics at BIT, supervised by Prof. Qing Shi (IEEE Senior Member) and Prof. Toshio Fukuda (2020 IEEE President).

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My research interests lie in the intersection of Robotics and Reinforcement Learning. I care about efficient, stable and safe robot performance in the unstructured open world.

Furthermore, we are passionate about releasing some interesting robotic environments, such as Bi-DexHands and SpaceRobotEnv. Hope everyone enjoys our work!

Introducing EfficientZero V2, a general framework designed for sample-efficient RL algorithms, which outperforms the SotA methods and DreamerV3 across diverse domains.

we present the i-Octree, a dynamic octree data structure that supports both fast nearest neighbor search and real-time dynamic updates, outperforming contemporary state-of-the-art approaches by achieving, on average, a 19% reduction in runtime on realworld open datasets.

We present a learning-based catching strategy, which can catch diverse objects of daily life with dexterous hands. The learned policies show strong zero-shot transfer performance on unseen objects.

Our approach enables the system's state to reach an equilibrium point within an optimal time and maintain stability there- after, referred to as "optimal-time stability". To achieve this, we integrate the optimization method into the Actor-Critic framework, resulting in the development of the Adaptive Lyapunov-based Actor-Critic (ALAC) algorithm.

Our algorithm (ESB-CPO) improves upon the trade-off between reducing constraint violations and improving expected returns in Safe Reinforcement Learning.

We propose a novel Learning-based Adaptive Compliance (LAC) algorithm to improve the efficiency and adaptability of symmetric bi-manual manipulation.

We propose the Intrinsically Motivated Adversarial Policy (IMAP) for efficient black-box evasion attacks in single- and multi-agent environments without any knowledge of the victim policy.

We developed a small-sized quadruped robotic rat (SQuRo), which includes four limbs and one flexible spine. On the basis of the extracted key movement joints, SQuRo was subtly designed with a relatively elongated slim body (aspect ratio: 3.42) and smaller weight (220 g) compared with quadruped robots of the same scale.

We propose a novel algorithm, EfficientLPT, to facilitate RL-based methods to improve planning accuracy efficiently. Our core contributions are constructing a mixed policy with prior knowledge guidance and introducing infinite norm to build a more reasonable reward function.

We propose a bimanual dexterous manipulation benchmark (Bi-DexHands) according to literature from cognitive science for comprehensive reinforcement learning research.

We developed a model-free Hierarchical Decoupling Optimization (HDO) algorithm to realize 6D-pose multi-target trajectory planning for the free-floating space robot.

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Last updated: Oct 15, 2023