Max Robotics — Global Service Robot Directory + US Certification

About

Xiaoyang Liu’s research bridges agricultural robotics and intelligent navigation, with a focus on computer vision, deep reinforcement learning, and path planning. His early work on apple harvesting robots introduced novel methods for fruit recognition under challenging conditions, such as nighttime segmentation using color and position information (53 citations) and a pulse-coupled neural network combined with a genetic Elman algorithm (36 citations) to improve harvesting efficiency. These contributions address critical bottlenecks in agricultural automation. Liu also co-developed ElegantRL-Podracer (12 citations), a scalable, cloud-native library for deep reinforcement learning that reduces data collection costs in complex environments like game playing and robotic control. More recently, his research on indoor mobile robots has advanced global path planning through map partitioning and preprocessing algorithms, enabling more efficient navigation for SLAM-based systems. With over 100 citations across his most-cited works, Liu’s interdisciplinary approach—from fruit detection to cloud-based RL—demonstrates a commitment to solving real-world challenges in robotics and automation, making his work valuable for students and researchers in agricultural tech, reinforcement learning, and autonomous systems.