Introduction

Since its establishment in 2005, the Advanced Robotics Laboratory has been conducting comprehensive research encompassing robotic analysis, precision control, perception and autonomy technologies, extending to applications in real-world industrial and daily environments.

Our research is broadly categorized into three core areas, wherein we pursue the synergistic integration of such fields.

1. Humanoids and Manipulators: this area studies robot control and human-robot collaboration.

2. Robotic Hands: this research is focused on grasping and in-hand control technologies to achieve human-level dexterous and precise manipulation. This is dedicated to developing a skin-integrated robotic hand platform.

3. Mobile Robots: this field studies active environmental perception and optimal path planning based on advanced sensors and autonomous navigation technologies.

Furthermore, our laboratory aims to realize safer and more practical next-generation intelligent robotic systems by actively incorporating state-of-the-art artificial intelligence (AI) technologies to advance various algorithms for perception, control, and actuation.

Recent News

• Lee, S., Park, J., Kim, M., & Cheong, J. (2026). Shattering Latency Boundaries: An LSTM–RCM Driven Smith Predictor for Path Tracking Control Under Time Delay. IEEE Transactions on Industrial Electronics.

• Cho, Y., Shoaib, M., & Cheong, J. (2025). A design method for tendon-driven serial manipulators using controllable block triangular form of structure null space matrix. Robotica, 1-25.

• Kim, M., Park, J., Shin, H., Seo, H., Il Park, D., Park, C., & Cheong, J. (2025). A two-wheeled robotic wheelchair with a slidable seat for elderly and people with lower limb disabilities. International Journal of Advanced Robotic Systems, 22(3), 17298806251339684.

• Jung, D., Gu, C., Park, J., & Cheong, J. (2024). Touch gesture recognition-based physical human-robot interaction for collaborative tasks. IEEE Transactions on Cognitive and Developmental Systems.

• Park, J., Kim, T., Gu, C., Kang, Y., & Cheong, J. (2024). Dynamic collision estimator for collaborative robots: A dynamic Bayesian network with Markov model for highly reliable collision detection. Robotics and Computer-Integrated Manufacturing, 86, 102692.

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