Jialun Cai

Papers

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About

Jialun Cai is a robotics researcher whose work centers on enabling mobile robots to perceive, map, and navigate safely in complex, dynamic environments. His primary research areas include simultaneous localization and mapping (SLAM), semantic scene understanding, and deep reinforcement learning for motion planning. Cai’s major contribution is the development of multimodal, semantic-aware systems that allow robots to function reliably in crowded or changing spaces—a critical step beyond traditional static-environment approaches. His most cited work, “MISD‐SLAM: Multimodal Semantic SLAM for Dynamic Environments” (25 citations), introduces a framework that integrates semantic information to filter dynamic objects, significantly improving mapping robustness in real-world settings. In “SafeCrowdNav: safety evaluation of robot crowd navigation in complex scenes” (8 citations), he applies deep reinforcement learning to address the challenge of safe, foresighted navigation among dense crowds. His paper “SPSD: Semantics and Deep Reinforcement Learning Based Motion Planning for Supermarket Robot” (3 citations) further demonstrates the practical application of these techniques in retail environments. Through this body of work, Cai is advancing the frontier of autonomous navigation, making robots more capable and trustworthy in the dynamic spaces where people live and work.