Yin Pok Chan

About

Yin Pok Chan is a robotics researcher specializing in the modeling, simulation, and control of complex, bio-inspired robotic systems. His work bridges the gap between theoretical control algorithms and practical hardware implementation, with a primary focus on cable-driven parallel robots (CDPRs) and musculoskeletal robots. Chan’s most notable contribution is the development of a "Tri-Space Operational Control" framework for redundant CDPRs, which addresses the challenge of managing actuation, joint, and operational constraints simultaneously. This work, published in 2023, has already garnered 12 citations for its novel iterative-learning-based reactive approach to trajectory tracking. In the domain of bio-robotics, Chan co-developed CARDSFlow, an open-source physics environment designed to streamline the design, simulation, and control of musculoskeletal robots. This end-to-end platform, cited 6 times, provides researchers with a unified tool to tackle the complexities of unilateral muscle-like actuation and high mechanical system complexity. Through his integration of control theory, simulation tools, and robotic design, Chan is contributing to the advancement of more dexterous, human-like robotic systems.

Research Focus

Key Achievements

Top Papers

1. 1
   Tri-Space Operational Control of Redundant Multilink and Hybrid Cable-Driven Parallel Robots Using an Iterative-Learning-Based Reactive Approach

   12 citations · 2023
2. 2
   CARDSFlow: An End-to-End Open-Source Physics Environment for the Design, Simulation and Control of Musculoskeletal Robots

   6 citations · 2018

Key Collaborators

Contact & Links