Kuang‐Hsing Chiang
Papers
1
Total Citations
11
H-Index
1
About
Kuang-Hsing Chiang is a researcher advancing the field of human-robot interaction, with a primary focus on robotic grasping, manipulation, and collaborative systems. His most notable contribution is the development of Fed-HANet, a federated visual grasping learning framework designed to enhance human-robot handovers—a critical capability for service robots in healthcare and logistics. This work, published in 2023 and already garnering 11 citations, addresses the challenge of object-agnostic, end-to-end planar grasping with up to six degrees of freedom, enabling robots to more seamlessly and safely transfer objects to human partners. Chiang’s research bridges the gap between machine learning and practical robotics, emphasizing real-world adaptability and privacy-preserving training through federated approaches. His work has significant implications for automating routine tasks in clinical and industrial settings, reducing physical strain on healthcare workers while improving efficiency. By tackling the complexities of dynamic handover scenarios, Chiang is helping to shape a future where robots can intuitively collaborate with humans, making his contributions both timely and impactful for students and researchers interested in embodied AI and assistive robotics.
Research Focus
Key Achievements
Top Papers
- 1Fed-HANet: Federated Visual Grasping Learning for Human Robot Handovers11 citations · 2023