Max Robotics — Global Service Robot Directory + US Certification

About

Huiliang Shang is a roboticist whose research spans agricultural automation, logistics, and bio-inspired locomotion. His work addresses fundamental challenges in robotic manipulation and perception, from pollination to bin packing. Shang’s 2024 paper on a redundant cooperative control strategy for robotic pollination (15 citations) tackles precision agriculture, while his 2022 deep reinforcement learning approach to online 3D bin packing (11 citations) advances warehouse automation by enabling robots to intelligently stack varied cartons in real time. He has also contributed foundational surveys, such as his 2021 review of point cloud registration (9 citations), which unifies multi-view data for robotic perception. Shang’s work on robotic grasping integrates attention-based deep Q-learning (2020, 9 citations) and low-cost vision solutions (2021, 4 citations), making manipulation more accessible. In bio-inspired robotics, he designed Pegasus, a quadruped with an underactuated wheeled-legged mechanism (2024, 4 citations), and a hopping robot using a six-bar linkage for enhanced terrain adaptability (2021, 4 citations). His hierarchical control of wheel-legged quadrupeds (2024, 6 citations) and novel 6D pose uncertainty distributions (2022, 4 citations) further demonstrate his breadth. With over 60 citations across ten papers, Shang’s work is shaping practical, intelligent robotics for agriculture, industry, and exploration.