Max Robotics — Global Service Robot Directory + US Certification

About

Peiliang Wu is a pioneering researcher in the field of service robotics, with a career spanning nearly two decades dedicated to advancing how robots perceive, learn from, and interact with human environments. His work centers on three key areas: robot perception and environmental mapping, human activity recognition, and the integration of large language models for robotic reasoning. Wu’s major contributions include developing novel approaches for cooperative localization in network robot systems and creating object-oriented holography maps that enable home robots to understand their surroundings more intuitively. He has also made significant strides in learning human daily behavior patterns using EM algorithms and detecting abnormal states in elderly individuals through fuzzy clustering methods—work that has direct applications in assistive healthcare robotics. With over 30 total citations across his most impactful papers, Wu’s research has steadily influenced the field. His recent work on MambaSlip, a multimodal large language model for real-time slip detection, and MACR-afford, a weakly supervised affordance grounding system, represents cutting-edge efforts to imbue robots with contextual reasoning capabilities. Wu’s sustained focus on making service robots more intelligent, context-aware, and capable of safe human interaction marks him as a dedicated contributor to the evolution of autonomous home assistance technologies.