Papers
62
Total Citations
930
H-Index
17
About
Ze Ji is a versatile robotics and artificial intelligence researcher whose work spans swarm intelligence, human-robot interaction, brain-computer interfaces, and autonomous systems. Best known for his influential 2020 paper introducing a Multi-Objective Particle Swarm Optimization approach to cooperative multi-robot task allocation — now cited over 159 times — Ji has consistently pushed the boundaries of intelligent robotic systems. His contributions to hierarchical reinforcement learning for multistep robotic manipulation (88 citations) and sim-to-real deep reinforcement learning for collision avoidance demonstrate a strong command of cutting-edge machine learning applied to real-world robotics challenges. Ji's research also reflects a deep commitment to human-centered technology. His early work through the ROBOSKIN project explored tactile interaction between humanoid robots and children with autism, addressing accessibility and therapeutic applications of robotics. Complementing this, his work on EEG-based motor imagery classification (91 citations) bridges neuroscience and assistive technology through brain-computer interfaces. From agricultural fruit segmentation using deep learning to bulk material payload prediction and fatigue monitoring, Ji's breadth is remarkable. His sustained output across European collaborative projects, autonomous systems, and applied AI marks him as an impactful researcher whose work meaningfully advances both theoretical understanding and practical deployment of intelligent robotic systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4
- 5
- 6
- 7
- 8DLNet: Accurate segmentation of green fruit in obscured environments24 citations · 2021
- 9
- 10