Jianbang Liu
Papers
11
Total Citations
170
H-Index
6
About
Jianbang Liu is a robotics and artificial intelligence researcher whose work sits at the intersection of autonomous navigation, machine learning, and medical robotics. He has made significant contributions to robot path planning, developing innovative approaches that leverage deep learning to overcome the limitations of classical algorithms. His 2021 paper on Conditional Generative Adversarial Networks for optimal path planning (56 citations) and his neural-network-driven prediction (NEED) framework (42 citations) demonstrate his ability to reframe longstanding computational challenges through modern AI techniques. Beyond navigation, Liu has extended his expertise to dynamic environments, proposing search-based trajectory planners for car-like robots and hierarchical deep reinforcement learning policies for complex multi-object rearrangement tasks. A notably practical dimension of his research addresses autonomous elevator operation for mobile robots, spanning button recognition and large-scale benchmarking datasets — work motivated in part by the pandemic-driven demand for service robots. Liu has also ventured into medical robotics, contributing a novel 3D curve-surface registration method for computer-assisted orthopedic surgery (17 citations) and a Bayesian optimization framework for surgical incision guidance in tracheotomy. With over 170 cumulative citations, his research reflects both technical depth and a consistent commitment to real-world robotic applicability.
Research Focus
Key Achievements
Top Papers
- 1Conditional Generative Adversarial Networks for Optimal Path Planning56 citations · 2021
- 2Robot Path Planning via Neural-Network-Driven Prediction42 citations · 2021
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- 10Learning-based Fast Path Planning in Complex Environments4 citations · 2021