Papers
6
Total Citations
66
H-Index
4
About
Yidao Ji’s research lies at the intersection of bio-inspired robotics, intelligent control, and networked systems, with a focus on enabling robots to learn, adapt, and operate reliably in complex environments. His most cited work, “Brain-Inspired Motion Learning in Recurrent Neural Network With Emotion Modulation” (44 citations), introduces a novel emotion-modulated learning rule for recurrent neural networks, allowing musculoskeletal and robotic arms to perform goal-directed tasks with high accuracy—a pioneering step toward emotionally intelligent robotic motion. Ji has also made significant contributions to fault detection and filtering for robotic manipulators under semi-Markov jump systems, developing mode-dependent event-triggered communication and quantization schemes that improve network efficiency and system robustness. His recent work extends to path planning for unmanned ground vehicles and space manipulators, employing improved A-star and GBNN algorithms to navigate unstructured and extraterrestrial environments. With over 60 citations across his publications, Ji is establishing himself as a rising researcher in intelligent robotics and control theory, bridging neural computation, nonlinear dynamics, and practical robotic applications.
Research Focus
Key Achievements
Top Papers
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