Weicai Ye
Papers
3
Total Citations
21
H-Index
3
About
Weicai Ye is a researcher advancing the frontiers of robot learning and multi-robot perception. His work centers on two key areas: collaborative dense reconstruction and the role of observation spaces in robotic manipulation. Ye’s most notable contribution is **Coxgraph**, a multi-robot system that achieves globally consistent, online dense reconstruction in real time. This work, with 12 citations, addresses critical challenges in time-sensitive scenarios like search and rescue, enabling multiple robots to collaboratively build a unified 3D map without sacrificing accuracy or speed. More recently, Ye has turned his attention to a fundamental question in robot learning: how the choice of observation space—whether 3D point clouds, images, or other modalities—impacts policy performance. His 2024 paper, *“Point Cloud Matters,”* challenges prevailing assumptions by systematically comparing these spaces, revealing that point clouds can offer significant advantages for certain manipulation tasks. This work has already garnered 9 citations, signaling its growing influence. By bridging robust geometric reconstruction with data-driven learning, Ye is helping to define how robots perceive and interact with the physical world, making his research essential reading for anyone working in embodied AI or multi-agent systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3