Chen-Kang Yang
Papers
1
Total Citations
11
H-Index
1
About
Chen-Kang Yang is a researcher at the forefront of human-AI interaction, specializing in brain-computer interfaces (BCI), AIoT (AI + Internet of Things), and collaborative robotics. His most cited work, “BCI-based hit-loop agent for human and AI robot co-learning with AIoT application” (2021), has garnered 11 citations and introduces a novel framework where human neural signals directly guide robot learning in real-time. This hit-loop agent architecture enables a closed feedback loop between human intent and machine action, allowing AI robots to adapt their behavior through continuous co-learning—a breakthrough for intuitive human-robot collaboration in smart environments. Yang’s contributions bridge cognitive neuroscience and edge computing, demonstrating how BCI can unlock seamless, hands-free control in AIoT systems. His work has significant implications for assistive robotics, adaptive manufacturing, and personalized smart homes, where machines learn not just from data but from human thought. By integrating neural decoding with IoT infrastructure, Yang is pioneering a future where humans and AI co-evolve through direct brain-to-machine dialogue, redefining the boundaries of interactive intelligence.
Research Focus
Key Achievements
Top Papers
- 1