Li Zhongxuan

I am currently pursuing a PhD in Robotics at the Department of Computer Science, The University of Hong Kong (HKU). In parallel, I am a visiting student at the Email Lab, Great Bay University. Prior to this, I completed an integrated MEng (with Bachelor's degree incorporated) in Electrical and Electronic Engineering at Imperial College London, where I was mentored by Prof. Petar Kormushev and Prof. Wei Dai.

In addition to my academic training, I have extensive industry experience. At HKU, I collaborate with TransGP through the InnoHK initiative. Before starting my doctoral studies, I worked at Huawei on optical network optimization and planning, and earlier completed an internship at Ocado Technology in the UK.

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I hold a broad interest in a variety of topics in robotics. In particular, I work on robotics task understanding/planning. My ultimate goal is to develop robots that can learn, reason, interact and evolve in real-world settings. Besides my profession, I have a great fondness about history and philosophy. Some papers are highlighted.

We present UniBiDex, a unified teleoperation framework for robotic bimanual dexterous manipulation that supports both VR-based and leader–follower input modalities. The framework integrates heterogeneous input devices into a shared control stack with consistent kinematic treatment and safety guarantees, employing null-space control to optimize bimanual configurations for smooth, collision-free motion.

We propose a hierarchical framework that integrates low-level action detection with high-level task reasoning to enable real-time sewn piece counting from video streams in garment manufacturing. Our method enhances Vision Transformer backbones with global cross-backbone layers and temporal ensembling for improved action detection, while using particle filtering on directed acyclic task graphs for robust repetition estimation despite execution variability.

We propose a new topological latent control model for reactive human–robot collaborative manipulation of deformable linear objects. The method achieves real-time collaborative control and has been recognized as an ESI Highly Cited + Hot Paper.

Our paper was selected for Poster Spotlight at AIAAT 2025, highlighting our research on unified teleoperation framework for robotic bimanual dexterous manipulation.

Led by Prof. Jia Pan at HKU, our Omni-Mani team achieved first place in the deformable object manipulation challenge, demonstrating advanced robotics capabilities in cable management tasks.

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