Keng Peng Tee

About

Keng Peng Tee is a prominent robotics researcher whose work spans human-robot interaction, adaptive control, and intelligent robotic systems. His most significant contributions lie at the intersection of game theory and physical human-robot collaboration, where he pioneered frameworks for shared control that allow robots to dynamically adapt their roles based on human intention. His 2015 paper on continuous role adaptation (186 citations) and subsequent game-theoretic framework for human-robot coordination (129 citations) established foundational methodologies for enabling intuitive, flexible collaboration between humans and robotic systems. Beyond shared control, Tee has made substantial contributions to adaptive and neural network-based control of robotic manipulators, addressing real-world challenges such as joint space constraints, uncertain dynamics, and task space compliance. His work on adaptive admittance control and model-free impedance control reflects a consistent drive toward safe, robust robot operation in unstructured environments. More recently, his research has extended into deep learning and sim-to-real transfer, exemplified by the KOVIS visual servoing system (52 citations), demonstrating versatility across emerging AI-driven robotics paradigms. With over 900 cumulative citations, Tee's research has meaningfully shaped modern robotics, making him an essential reference for students studying human-robot interaction and intelligent control systems.

Research Focus

Key Achievements

Top Papers

1. 1
   Continuous Role Adaptation for Human–Robot Shared Control

   186 citations · 2015
2. 2
   A model of force and impedance in human arm movements

   163 citations · 2004
3. 3
   A Framework of Human–Robot Coordination Based on Game Theory and Policy Iteration

   129 citations · 2016
4. 4
   Adaptive bias RBF neural network control for a robotic manipulator

   116 citations · 2021
5. 5
   Adaptive admittance control of a robot manipulator under task space constraint

   91 citations · 2010
6. 6
   Adaptive neural control for an uncertain robotic manipulator with joint space constraints

   89 citations · 2015
7. 7
   KOVIS: Keypoint-based Visual Servoing with Zero-Shot Sim-to-Real Transfer for Robotics Manipulation

   52 citations · 2020
8. 8
   Reinforcement learning control for coordinated manipulation of multi-robots

   31 citations · 2015
9. 9
   Model-free impedance control for safe human-robot interaction

   29 citations · 2011
10. 10
    Role adaptation of human and robot in collaborative tasks

    26 citations · 2015

Key Collaborators

Contact & Links