Kexun Chen
Papers
2
Total Citations
12
H-Index
2
About
Kexun Chen is a rising researcher in embodied AI and multi-robot systems, with a focus on semantic navigation and human-robot collaboration. His work bridges the gap between visual-language understanding and autonomous decision-making in complex, unstructured environments. In his 2024 paper "VLAI," Chen introduced a novel framework that aligns visual and language information to guide robotic object goal navigation, enabling robots to more effectively balance exploration and exploitation—a fundamental challenge in household robotics. Building on this, his 2025 work "Enhancing Multi-Robot Semantic Navigation Through Multimodal Chain-of-Thought Score Collaboration" pioneers a cooperative reasoning approach that allows multiple robots to share semantic knowledge and jointly decide on navigation directions, moving beyond traditional single-robot centralized planning. This work addresses a critical bottleneck in multi-robot systems for domestic service, where understanding how humans naturally collaborate to explore unfamiliar spaces is essential. Though early in his career, Chen’s papers have already garnered 6 citations each, signaling growing recognition. His contributions are laying the groundwork for more intelligent, collaborative robotic assistants capable of navigating and serving in real-world homes.
Research Focus
Key Achievements
Top Papers
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- 2