Yaohua Guo
Papers
1
Total Citations
5
H-Index
1
About
Yaohua Guo is a researcher advancing the intersection of reinforcement learning and multi-robot systems, with a primary focus on safe, distributed motion planning. In their most-cited work, "Distributed safe reinforcement learning for multi-robot motion planning" (2021), Guo developed a novel algorithm that enables multiple mobile robots to achieve optimal goal-reaching while guaranteeing collision avoidance at all times. This work is distinguished by its theoretical contributions, including rigorous proofs of neural network weight convergence, ensuring both suboptimal performance and "anytime" safety—a critical requirement for real-world autonomous systems. With 5 citations, this foundational paper has already begun to influence the growing field of safe multi-agent learning. Guo’s research is particularly relevant for applications in warehouse automation, drone swarms, and autonomous exploration, where coordinating multiple agents without compromising safety is paramount. By bridging reinforcement learning with formal safety guarantees, Guo is helping to make multi-robot systems more reliable and deployable in complex, dynamic environments. Their work represents an important step toward practical, scalable autonomy.
Research Focus
Key Achievements
Top Papers
- 1Distributed safe reinforcement learning for multi-robot motion planning5 citations · 2021