Papers
16
Total Citations
424
H-Index
11
About
Lu Dong is a prominent robotics researcher whose work sits at the intersection of motion planning, reinforcement learning, and intelligent control systems. His research has made substantial contributions to advancing autonomous robot operation, spanning mobile robot navigation, robotic manipulator control, and multi-agent coordination. Dong's most influential work, a 2023 review of mobile robot motion planning methods (113 citations), has become a key reference for researchers bridging classical planning algorithms with deep reinforcement learning architectures. This reflects his broader research philosophy: integrating traditional control theory with modern machine learning to tackle real-world complexity. His development of actor-critic learning frameworks for robotic manipulators — addressing prescribed constraints, elastic joint dynamics, and dual-arm coordination with hysteresis — has earned significant recognition, with multiple papers exceeding 40 citations. Particularly noteworthy is his pioneering work on multi-agent systems. His multiagent Soft Actor-Critic hybrid motion planner (47 citations) enables robust multi-robot coordination without explicit communication, a meaningful advance for practical deployments. More recently, Dong has extended this into socially aware robot navigation among pedestrians and multi-task reinforcement learning with attention mechanisms, demonstrating a continuously evolving research agenda. With over 400 cumulative citations, his work is shaping the future of intelligent, autonomous robotic systems.
Research Focus
Key Achievements
Top Papers
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- 3Multiagent Soft Actor-Critic Based Hybrid Motion Planner for Mobile Robots47 citations · 2022
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- 6Multi-Task Reinforcement Learning With Attention-Based Mixture of Experts25 citations · 2023
- 7Event-triggered receding horizon control via actor-critic design19 citations · 2020
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