Tran Duy Hoa
Papers
2
Total Citations
33
H-Index
2
About
Tran Duy Hoa is a pioneering researcher at the intersection of neuroscience and robotics, whose work focuses on developing biologically inspired control systems for humanoid robots. His major contributions lie in two key areas: fall prevention during dynamic locomotion and unified action representation for robotic systems. In his highly cited 2018 paper (22 citations), Hoa introduced a novel approach combining Q-learning reinforcement learning with self-organizing maps (SOM) to enable humanoid robots to detect and recover from perturbations during swinging motions—a critical advancement for stable bipedal locomotion. His 2019 work (11 citations) represents a significant theoretical breakthrough, proposing a "Concrete Action Representation Model" that draws directly from hierarchical neural control mechanisms in the human cortex and spinal cord. This unified framework bridges the gap between neuroscience and robotics, offering a single computational architecture capable of generating actions for both locomotion and manipulation tasks. Hoa’s research is notable for its interdisciplinary approach, translating complex neural principles into practical robotic algorithms, and his work continues to influence the development of more resilient, human-like robotic systems capable of operating in dynamic, real-world environments.
Research Focus
Key Achievements
Top Papers
- 1A Humanoid Robot Learns to Recover Perturbation During Swinging Motion22 citations · 2018
- 2Concrete Action Representation Model: From Neuroscience to Robotics11 citations · 2019