Yihang Tao
Papers
1
Total Citations
17
H-Index
1
About
Yihang Tao is a leading researcher in collaborative perception and task-oriented communications for multi-agent systems, with a focus on real-time adaptability and robustness in dynamic environments. His most-cited work, "R-ACP: Real-Time Adaptive Collaborative Perception Leveraging Robust Task-Oriented Communications" (2025, 17 citations), addresses a critical challenge in autonomous systems: maintaining accurate perception when mobile robots or connected vehicles face extrinsic calibration errors due to motion or non-rigid sensor mounts. Tao’s key contributions include developing a framework that integrates online calibration with task-driven communication protocols, enabling agents to selectively share only the most relevant perceptual data while compensating for misalignment in real time. This work significantly improves sensing range and accuracy in multi-robot and vehicular networks, directly impacting applications in autonomous driving and swarm robotics. By bridging the gap between communication efficiency and perception reliability, Tao’s research has been recognized for its practical implications in next-generation intelligent transportation systems. His approach to robust, adaptive collaboration continues to influence the design of scalable, fault-tolerant multi-agent perception systems.
Research Focus
Key Achievements
Top Papers
- 1