I am a tenure-track Assistant Professor in the Department of Mechanical Engineering at the National University of Singapore (NUS), where I am establishing the Grasping and Manipulation Laboratory (G&M Lab), launching in April 2026. Currently, I am a Postdoctoral Researcher in the Mechanical Systems Control Lab at the University of California, Berkeley, working with Prof. Masayoshi Tomizuka. Prior to Berkeley, I served as a Research Assistant Professor at The Chinese University of Hong Kong (CUHK) after a short postdoctoral appointment in the same department working with Prof. Yunhui Liu. I received my Ph.D. degree in Mechanical Engineering from The University of Hong Kong, where I was advised by Prof. Yonghua Chen and worked closely with Prof. Kazuhiro Kosuge.

I have authored more than 70 peer-reviewed publications with over 1,700 citations (h-index: 20, i10-index: 34), including over 10 first-author papers in leading journals such as IEEE Transactions on Robotics, IEEE/ASME Transactions on Mechatronics, and Soft Robotics. I hold two granted and three published U.S. patents, with additional filings currently under review. My contributions have been recognized with six Best Conference Paper Awards or Finalist honors and the Gold Medal at the Geneva International Exhibition of Inventions. I am a recipient of the ASME DSCD Rising Star Award and have been recognized as an IEEE Robotics and Automation Letters Outstanding Reviewer. I have also taken on active professional service roles at major international conferences, including Session Chair, Associate Editor, Registration Chair, and Award Chair for IEEE ICRA, IEEE RO-MAN, IEEE ICCA, and IEEE RCAR. I have secured over HKD one million in competitive research funding, including support from Hong Kong’s General Research Fund (GRF) as Principal Investigator.

Recruitment (Ph.D. Students, Postdoctoral Researchers, RAs, and Interns)

I am recruiting Ph.D. students (Latest round, Fall 2026, Spring, Fall 2027), postdoctoral researchers, and research interns who are eager to build the next generation of robotic systems. If you enjoy designing and building real robots, inventing new actuation and transmission mechanisms, integrating sensing directly into hardware, and developing embodied intelligence through the integration of classical control and data-driven policies grounded in physical interaction, the G&M Lab offers an environment where your ideas can mature into impactful, system-level research.

Research Focuses

1. Robotic Hands: Core Mechanisms, Actuation, and Transmission

• Fundamental mechanisms for dexterous robotic hands and manipulators

• Novel actuation and transmission architectures, including soft–rigid hybrid and variable-stiffness designs

• Reconfigurable and modular hand systems enabling adaptable morphology and functionality

• Embodied mechanical intelligence, where structure, compliance, and dynamics directly support manipulation capabilities
  
  

2. Multimodal Perception: Vision–Tactile–Proprioceptive Sensing

• Hardware-integrated tactile and proprioceptive sensing tightly coupled with hand structures

• Multimodal vision–tactile perception for contact understanding and state estimation

• Physically grounded perception of contact, force, and deformation

• Learning-aware sensor–structure co-design for robust perception in unstructured environments
  
  

3. Human–Robot Interaction and Data-Centric Interfaces

• Human–robot interfaces for dexterous manipulation and high-fidelity skill transfer

• Data gloves and wearable sensing systems for precise capture of hand motion and interaction forces

• Teleoperation frameworks for efficient collection of high-quality, task-relevant manipulation data

• Interface designs that bridge human embodiment and robotic morphology
  
  

4. Manipulation Algorithms and Data-Driven Policies

• Control and planning algorithms for contact-rich manipulation and in-hand dexterity

• Model-based and hybrid control frameworks incorporating physical priors

• Data-driven manipulation policies enabled by teleoperation, demonstration, and self-generated interaction data

• Integration of physical models, task representations, and learning-based visuomotor policies for skill acquisition and generalization
  
  

Experimental Platforms and Infrastructure

The lab will be equipped with a comprehensive set of experimental platforms and computational resources, including legged humanoid robots, mobile-based humanoid systems, multiple robotic manipulators such as Franka Emika and Universal Robots, a wide range of mainstream robotic hands and grippers, and multimodal sensing systems for vision, tactile, and force/torque perception.

Rather than pursuing large-scale generic data collection, the lab emphasizes hardware-driven, task-specific data generation, where novel robotic hands, sensors, and interaction mechanisms enable post-training and task-adaptive manipulation policies. Dedicated computing resources support real-time control, physics-grounded learning, and rapid policy adaptation tightly coupled with new hardware designs.

Top-Tier University Collaborations and Visiting

I have established close research collaborations and visiting ties with leading groups at top-tier universities, including the University of California, Berkeley, Stanford University, Carnegie Mellon University, The Chinese University of Hong Kong, and Shanghai Jiao Tong University, among others. These collaborations provide opportunities for student exchanges and research visits, enabling students to gain international research experience and engage in joint projects with world-leading laboratories.

Join Us

If you are passionate about building robots that can truly understand, interact with, and manipulate the physical world, I warmly welcome you to join the NUS Grasping and Manipulation Lab starting Spring 2026.

Please feel free to reach out with your CV, research interests, and relevant project experience.