Max Robotics — Global Service Robot Directory + US Certification

About

Yuxuan Liu is a computer vision and robotics researcher whose work sits at the intersection of autonomous driving, 3D perception, and intelligent robotic systems. His research focuses on developing practical, low-cost solutions for real-world autonomy challenges — particularly leveraging camera-based sensing as an alternative to expensive LiDAR systems. Liu's most influential contribution, "Ground-Aware Monocular 3D Object Detection for Autonomous Driving" (2021, 160+ citations), advanced the field by exploiting geometric ground-plane constraints to dramatically improve 3D object localization from a single RGB camera. Complementing this, his "YOLOStereo3D" framework (83 citations) offered an efficient stereo-based 3D detection approach, while his self-supervised depth prediction work further broadened his impact on affordable perception pipelines. Beyond detection, Liu has made notable contributions to autonomous control through deep imitative reinforcement learning for car racing (82 citations), robotic waste sorting for construction sites (125 citations), and multi-sensor SLAM dataset development. His work on knowledge distillation for incremental 3D learning also reflects a forward-thinking interest in continual robot intelligence. With over 500 cumulative citations across diverse robotics subfields, Liu has established himself as a versatile and impactful researcher shaping the future of cost-effective autonomous systems.