Papers
1
Total Citations
21
H-Index
1
About
Yihao Sun is a rising researcher at the intersection of robotics, artificial intelligence, and bio-inspired engineering, with a primary focus on autonomous underwater systems. His most notable contribution is the development of an end-to-end formation control framework for robotic fish using deep reinforcement learning enhanced by non-expert imitation—a breakthrough that bridges the gap between complex simulation and real-world deployment. This work, published in 2023 and already garnering 21 citations, demonstrates how imitation learning can accelerate policy training without requiring expert demonstrations, making swarm robotics more accessible and scalable. Sun’s research addresses fundamental challenges in multi-agent coordination, particularly in fluid environments where traditional control methods falter. By integrating reinforcement learning with biomimetic design, he has opened new pathways for adaptive, decentralized control in underwater exploration and environmental monitoring. His approach not only reduces the computational burden of training but also improves the robustness of robotic fish in dynamic currents. As a young scholar, Sun’s work signals a shift toward data-driven, imitation-assisted autonomy, positioning him as a key contributor to the future of intelligent, nature-inspired robotics.
Research Focus
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Top Papers
- 1