Liangliang Lu

Huazhong University of Science and Technology

Papers

Total Citations

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About

Liangliang Lu is a robotics researcher whose work focuses on the intersection of reinforcement learning and multi-robot systems, with a particular emphasis on autonomous manipulation and distributed control. His most-cited paper, "A Method of Robot Grasping Based on Reinforcement Learning" (2022), introduces a novel approach that leverages a six-degree-of-freedom robot and an RGB-D camera to execute grasping actions without relying on traditional model-based methods, marking a significant step toward more adaptive and intelligent robotic manipulation. In his equally influential work, "An Improved Multi-robot Distributed Formation Tracking Control Based on Sensor Self-calibration: System Design and Experimental Study" (2021), Lu addresses a critical challenge in multi-robot coordination by proposing a distance-based formation control method that eliminates the need for external positioning equipment like optical motion capture systems. This innovation enhances the practicality and scalability of swarm robotics in real-world environments. Though his citation counts are still growing, Lu's contributions are notable for their focus on sensor self-calibration and model-free learning, positioning him as an emerging voice in autonomous robotics. His work is particularly valuable for researchers exploring how robots can learn and cooperate in unstructured settings.