Papers
9
Total Citations
124
H-Index
7
About
Cheng-Kai Lu is a researcher at the forefront of intelligent robotics and autonomous systems, with a focus on deep reinforcement learning, computer vision, and human-robot interaction. His work spans assistive robotics, autonomous navigation, and medical image analysis, demonstrating a remarkable breadth of application. Lu’s major contributions include designing adaptive guidance systems for assistive robotic walkers and developing novel end-to-end deep reinforcement learning architectures for collision-free autonomous navigation of tracked robots in complex environments. In the medical domain, he has advanced automatic polyp segmentation in colonoscopy images using modified deep convolutional encoder-decoder architectures, contributing to colorectal cancer diagnosis. His research also extends to creative applications, such as a Chinese calligraphy-writing robotic system that learns stroke trajectories autonomously. With over 120 total citations across his most-cited papers, including 30 for his adaptive guidance system and 28 for his deep reinforcement learning navigation work, Lu’s impact is evident. Notably, his 2023 work on a vision-based mobile collaborative robot incorporating a multicamera localization system addresses key challenges in Industry 4.0, while his 2025 research on skeleton-based human action recognition using LSTM and depthwise separable CNNs continues to push boundaries in human-robot interaction.
Research Focus
Key Achievements
Top Papers
- 1Adaptive guidance system design for the assistive robotic walker30 citations · 2015
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- 4Autonomous Visual Navigation using Deep Reinforcement Learning: An Overview13 citations · 2019
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