Max Robotics — Global Service Robot Directory + US Certification

About

Sangjae Bae is a researcher advancing the frontiers of safe and intelligent autonomy, with a focus on human-robot interaction, multi-robot coordination, and automated driving. His core contributions lie in developing decision-making and motion planning frameworks that robustly handle uncertainty—whether from unknown human intentions, behavioral predictions, or competitive scenarios. Bae’s most impactful work, "Active uncertainty reduction for safe and efficient interaction planning" (2023, 19 citations), introduces a shielding-aware dual control approach that actively reduces prediction uncertainty to enhance safety and efficiency in interactive settings. He has also pioneered novel mission-planning strategies for heterogeneous multi-robot teams using LLM-constructed hierarchical trees (2025, 4 citations), enabling complex task decomposition under robot-specific constraints. In automated driving, Bae addresses behavioral uncertainties through delayed-decision motion planning that reasons over multiple predictions (2025, 2 citations), and explores game-theoretic strategies for competitive racing scenarios (2024, 1 citation). His work bridges theoretical rigor with practical deployment, earning recognition for its impact on safe human-robot collaboration and autonomous navigation. Bae’s research continues to shape how robots interact intelligently and safely in uncertain, dynamic environments.