Jaeseok Heo

Papers

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About

Jaeseok Heo is a rising researcher at the forefront of socially-aware robotics and multi-agent systems. His work centers on enabling robots to navigate crowded human environments with safety and social grace—a critical challenge as autonomous systems integrate into daily life. Heo’s most influential contribution is the SCAN framework (Socially-Aware Navigation Using Monte Carlo Tree Search), which has garnered 10 citations since 2023. SCAN introduces a novel global planning approach that uses Monte Carlo Tree Search to anticipate pedestrian discomfort, allowing robots to chart paths that respect human social norms without sacrificing efficiency. Building on this, Heo’s recent work, MAC-ID (Multi-Agent Reinforcement Learning with Local Coordination for Individual Diversity), explores how multiple robots can coordinate their movements while preserving behavioral diversity—a key step toward scalable, human-friendly robot teams. Though early in his career, Heo’s research directly addresses the practical tension between robotic utility and human comfort, making his work essential reading for students and engineers developing navigation systems for airports, shopping centers, and other bustling spaces. His trajectory signals a deep commitment to human-centered robotics.