Max Robotics — Global Service Robot Directory + US Certification

About

Tianyang Duan is an emerging robotics and artificial intelligence researcher whose work bridges autonomous navigation, deep reinforcement learning robustness, and distributed computing for robotic systems. His most impactful contributions center on air-ground robot (AGR) navigation in complex, occluded environments, where he has developed novel systems including AGRNav and HE-Nav — frameworks that deliver efficient, energy-conscious path planning in cluttered settings such as dense forests and large-scale buildings, garnering 8 and 6 citations respectively since their 2024 publication, a remarkable pace for recent work. Equally significant is Duan's sustained focus on the vulnerability of deep reinforcement learning (DRL) in real-world robotics deployments. Through a series of 2025 publications, he has pioneered adversarial attack methodologies that evaluate and strengthen DRL robustness — examining policy distributions, gradient-masked perturbations, and state-aware optimization strategies — collectively accumulating 10 citations within months of release. Earlier work on ROG, a distributed training system for robotic IoT environments, demonstrates his broader interest in scalable, resilient machine learning infrastructure. Across these domains, Duan's research consistently addresses the gap between controlled laboratory performance and reliable real-world robotic deployment, making his work particularly relevant to researchers and engineers advancing autonomous systems.