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Ziwei Wang supervises 2 postgraduate research students. If these students have produced research profiles, these are listed below:

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Dr Ziwei Wang

Lecturer in Robotics

Ziwei Wang

D11, D - Floor

Engineering Building

LA1 4YR

Lancaster

Tel (mobile): +44 (0)7707 639777

Research overview

  • Physical human-robot interaction
  • Sensory and motor augmentation in teleoperation
  • Haptic communication between humans and robots 
  • Game theory-based interaction control
  • Data-driven neuromechanics modelling
  • Machine learning applications in robotics
  • Robotic rehabilitation and assessment

PhD supervision

If you are interested in doing a Ph.D. on Human-Robot Interaction or Intelligent Control (details of research topics can be found on Research Interests) or postdoctoral Fellowships via external funding sources (e.g., Marie-Curie). Please send a copy of your CV and a summary of your research interests.

Profile

Ziwei Wang received the Ph.D. degree from the Department of Automation, Tsinghua University, China, in 2020. During the period of 2020 and 2022, he was a Research Associate with Human Robotics Group, the Department of Bioengineering, Imperial College London, U.K. Since 2022, he has joined Lancaster University, U.K, as a Lecturer in Robotics. His research interests focus on bilateral teleoperation, fuzzy control, and human-robot systems, aiming at enhancing human sensorimotor capability and overall robotic system performance.

Ziwei is enthusiastic about developing AI-based tele-robotics and control technologies to overcome the challenges in remote manipulation so that the potential benefits of intelligent teleoperator augmentation may be achieved or extended for remote surgery or space assembly. 1) Prescribed-Performance Teleoperation: A novel concept (termed as practically prescribed-time stability) and systematic approach for general teleoperation systems, which ensured user-defined characteristics of performance metrics such as operation time, accuracy, and state constraints. 2) Haptic Augmentation: He developed a Gaussian-process-based classifier for interactive environment identification and polynomial fuzzy based force estimation. These techniques have been successfully tested in the peg transfer task via Da Vinci surgical robot, which enables a robust estimation accuracy (<0.025N) with few-shot data through virtues of the fitting ability of polynomials and the nonlinear expression ability of fuzzy models. 3) Multiple Cooperative Teleoperation: He developed a series of reinforcement learning mechanisms to improve operation expertise and correct the operator commands before driving the remote devices, which allows for human-human and human-robot interaction in extreme environments.

He has authored/co-authored over 40 publications in relevant fields. He serves as an editorial board, a program committee member, and a reviewer for various international journals and international conferences. He was the Co-Investigator of Future AI and Robotics for Space (FAIR-SPACE) Hub. He has organized special sessions for IEEE SMC 2021, IROS Workshop 2021, and IEEE WCCI 2022. He has been the Guest and Review Editor for Frontiers in Robotics and AI. He is an IEEE Member and a CSF Member.

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