Papers
5
Total Citations
90
H-Index
4
About
Minkyu Choi is a leading researcher in cognitive robotics, specializing in the intersection of predictive coding, deep learning, and visuomotor coordination. His work focuses on how robots can develop human-like cognitive functions—such as recognizing, predicting, and generating actions—through iterative learning of dynamic perceptual patterns. Choi’s major contribution is the development of predictive coding-based deep dynamic neural networks that enable robots to coordinate vision, proprioception, and action in real time. His most cited paper (2018, 51 citations) investigates imitative interaction between robots and humans, demonstrating how mental simulation of actions can emerge from learning spatio-temporal patterns. Another key work (2017, 18 citations) presents a dynamic neural network model for visuomotor learning, while his 2015 study (13 citations) introduces the Visuo-Motor Deep Dynamic Neural Network (VMDNN), achieving “synergy” in cognitive behavior by integrating visual recognition, attention, and action generation. Choi’s research has profound implications for developing autonomous robots capable of goal-directed behavior and natural human-robot interaction, making him a pivotal figure in advancing embodied AI and cognitive robotics.
Research Focus
Key Achievements
Top Papers
- 1
- 2Predictive coding-based deep dynamic neural network for visuomotor learning18 citations · 2017
- 3
- 4
- 5