Max Robotics — Global Service Robot Directory + US Certification

About

Shuang Lu is a leading researcher at the intersection of robotics, human-robot interaction, and multimodal machine learning, with a focus on making industrial and service robots more intuitive and adaptive. Their most-cited work, a comprehensive review of interfaces for industrial human-robot interaction (93 citations), establishes a foundational taxonomy of speech and gesture-based control systems. Lu’s pioneering contribution to cross-modal perception is demonstrated in their highly influential paper on visuo-tactile object recognition (57 citations), where they developed a framework enabling robots trained solely on visual data to recognize objects through touch alone—a breakthrough for robotic dexterity. More recently, Lu has advanced robot learning from multi-modal demonstrations and natural language instructions, addressing the critical challenge of re-programming collaborative robots for small and medium manufacturers. Their work also extends to emotion-driven interaction for nursing care and intervention strategies for autonomous mobile robots. With a growing citation impact and a portfolio spanning from foundational reviews to cutting-edge multimodal learning, Lu is shaping the future of flexible, human-aware robotics.