Max Robotics — Global Service Robot Directory + US Certification

About

Yuanfu Luo is a robotics researcher whose work sits at the intersection of autonomous driving, robot planning under uncertainty, and multi-agent motion modeling. His research addresses one of the field's most demanding challenges: enabling robots and autonomous vehicles to make safe, real-time decisions in complex, unpredictable environments populated by many dynamic agents. Luo's most celebrated contribution is PORCA (Pedestrian Optimal Reciprocal Collision Avoidance), a motion prediction and planning framework for autonomous driving among dense crowds of pedestrians. By modeling both a pedestrian's global navigation intention and their local interactions, PORCA significantly advanced the realism of pedestrian modeling for autonomous systems, accumulating over 200 citations. Complementing this, his work on HyP-DESPOT introduced a hybrid parallel algorithm that dramatically accelerates online planning under uncertainty, making principled POMDP-based decision-making more computationally tractable for real-world robotics. His LeTS-Drive system further pushed the frontier by integrating learning with tree search to handle crowded intersection scenarios. Across his portfolio, Luo has contributed over 390 citations, reflecting genuine influence on how the robotics community approaches uncertainty-aware, crowd-aware autonomous navigation — a problem central to the safe deployment of self-driving technology.