Papers
2
Total Citations
7
H-Index
2
About
Sean Ye is a rising star in robotics and artificial intelligence, whose research is redefining how autonomous systems perceive, predict, and act in complex, dynamic environments. His work masterfully bridges the gap between generative modeling and reinforcement learning, with a core focus on trajectory forecasting, motion planning, and multi-agent decision-making. Ye’s pioneering 2024 paper, "Efficient Trajectory Forecasting and Generation with Conditional Flow Matching," introduces a unified framework that achieves state-of-the-art performance by treating prediction and generation as a single, versatile task—a significant leap over prior, siloed approaches. He further advances the field with "Diffusion-Reinforcement Learning Hierarchical Motion Planning in Multi-agent Adversarial Games," where he tackles the formidable challenge of planning for an evasive target in partially observable pursuit-evasion scenarios. By integrating diffusion models with hierarchical RL, Ye provides a novel solution for high-stakes, adversarial robotics. Though early in his career, his work has already garnered attention, and his innovative fusion of generative AI with control theory positions him as a key figure to watch in the next generation of autonomous systems research.
Research Focus
Key Achievements
Top Papers
- 1
- 2