Papers
6
Total Citations
155
H-Index
4
About
Yiliang Liu is a multidisciplinary researcher whose work bridges human-computer interaction, robotics, and remote sensing, with particular focus on brain-computer interfaces (BCIs) and autonomous navigation systems. His most significant contributions lie in developing innovative human-robot hybrid systems that harness neural signals — including motor imagery and steady-state visually evoked potentials — to enable intuitive teleoperation of mobile robots in complex, unknown environments. By integrating electroencephalogram (EEG)-based brain-robot interfaces with cutting-edge techniques such as deep learning and simultaneous localization and mapping (SLAM), Liu has helped advance the frontier of intelligent, adaptive robotic navigation. His 2019 paper on a human-robot hybrid system incorporating deep learning SLAM has garnered 53 citations, reflecting strong community interest in this convergence of neuroscience and robotics. Beyond robotics, Liu has made notable contributions to planetary science, authoring a widely cited self-calibration bundle adjustment method for processing China's Chang'E-2 lunar stereo imagery, which earned 48 citations and demonstrated his versatility across engineering domains. Collectively, his body of work positions him as a productive contributor to assistive robotics, neural engineering, and space exploration technologies.
Research Focus
Key Achievements
Top Papers
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- 4Brain Teleoperation of a Mobile Robot Using Deep Learning Technique7 citations · 2018
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- 6An Indoor Navigation Control Strategy for a Brain-Actuated Mobile Robot2 citations · 2018