Yan Chang

Papers

Total Citations

H-Index

About

Yan Chang is a rising robotics researcher whose work focuses on enabling robots to navigate and reason over complex, long-horizon environments. Their key research areas include spatio-temporal memory, world modeling, and end-to-end generalizable navigation. Chang’s major contribution is the development of **ReMEmbR**, a system that allows robots to build and reason over long-horizon spatio-temporal memory, enabling them to answer nuanced questions about where and when events occurred in their environment—a critical step toward truly autonomous, context-aware robots. This work has already garnered 7 citations since its 2025 publication, signaling strong early impact. Additionally, Chang’s **X-MOBILITY** framework tackles the challenge of general-purpose navigation in cluttered, unstructured settings by combining world modeling with end-to-end learning, overcoming limitations of both classical and learning-based approaches. Together, these contributions address fundamental gaps in robot autonomy, from memory-driven reasoning to robust navigation. Chang’s work is particularly notable for bridging high-level reasoning with low-level control, promising more capable and interactive robots for real-world applications.