Kuu‐Young Young

About

Kuu-Young Young is a leading researcher in assistive robotics and intelligent control systems, with a career dedicated to enhancing human-robot interaction for mobility and rehabilitation. His most impactful work centers on walk-assist robots designed for the elderly, where he pioneered a dual-mode control strategy combining passive and active guidance to ensure user safety and intuitive maneuverability. With 50 citations, his 2012 paper on outdoor guidance established a foundation for human-friendly robotic walkers, while his subsequent 2015 study on intention-based maneuvering (23 citations) refined how these robots interpret and follow user commands. Young also advanced navigation control, developing an interval type-2 fuzzy controller optimized by dynamic-group particle swarm optimization (43 citations), and explored VR-based teleoperation for compliance tasks (33 citations). His contributions extend to EMG-based control systems, where adaptive neural fuzzy inference and empirical mode decomposition enable upper-limb motion governance (21 citations). Early in his career, he tackled fundamental challenges in robot compliance and impact control, integrating reinforcement learning and bio-inspired reflex schemes (21 and 17 citations). Through his work on calibration models and braking controllers, Young has consistently prioritized safety and adaptability, making him a key figure in the development of intelligent, human-centric robotic systems for aging populations.

Research Focus

Key Achievements

Top Papers

1. 1
   Active and Passive Control of Walk-Assist Robot for Outdoor Guidance

   50 citations · 2012
2. 2
   Navigation Control of Mobile Robots Using an Interval Type-2 Fuzzy Controller Based on Dynamic-group Particle Swarm Optimization

   43 citations · 2018
3. 3
   VR-Based Teleoperation for Robot Compliance Control

   33 citations · 2001
4. 4
   Effective Maneuver for Passive Robot Walking Helper Based on User Intention

   23 citations · 2015
5. 5
   An adaptive upper-arm EMG-based robot control system

   21 citations · 2010
6. 6
   Reinforcement Learning and Robust Control for Robot Compliance Tasks

   21 citations · 1998
7. 7
   Upper-Limb EMG-Based Robot Motion Governing Using Empirical Mode Decomposition and Adaptive Neural Fuzzy Inference System

   18 citations · 2012
8. 8
   An impact control scheme inspired by human reflex

   17 citations · 1996
9. 9
   Walk-Assist Robot: A Novel Approach to Gain Selection of a Braking Controller Using Differential Flatness

   17 citations · 2013
10. 10
    Implementation of a Variable D-H Parameter Model for Robot Calibration Using an FCMAC Learning Algorithm

    17 citations · 1999

Key Collaborators

Contact & Links