Papers
17
Total Citations
267
H-Index
9
About
Guoliang Liu is a robotics and artificial intelligence researcher whose work spans hierarchical reinforcement learning, robot navigation, visual localization, and human-robot collaboration. His most influential contribution, "Hierarchical Reinforcement Learning With Universal Policies for Multistep Robotic Manipulation" (2021, 88 citations), demonstrates his ability to bridge symbolic planning and low-level motion control — a challenging problem central to autonomous manipulation tasks like block stacking and parts assembly. Complementing this, his deep reinforcement learning approach to visual path-following for industrial robots reflects a sustained commitment to making robotic systems more adaptive and practically deployable. Liu has also made notable strides in robot safety and navigation, developing danger-index-based obstacle avoidance strategies and real-time collision avoidance frameworks for human-robot collaborative environments. His work on Visual SLAM and semantic-driven indoor localization addresses critical challenges in robust robot perception under dynamic, real-world conditions. More recently, his contributions to augmented reality-based robot programming and agricultural fruit segmentation illustrate a broadening research vision. With over 240 cumulative citations across diverse topics, Liu represents a versatile and impactful voice in applied robotics research, consistently connecting theoretical innovation with real-world engineering challenges.
Research Focus
Key Achievements
Top Papers
- 1
- 2Smart Obstacle Avoidance Using a Danger Index for a Dynamic Environment42 citations · 2019
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- 4Visual SLAM Based on Dynamic Object Removal18 citations · 2019
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- 7FCOS-EAM: An accurate segmentation method for overlapping green fruits13 citations · 2024
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