Max Robotics — Global Service Robot Directory + US Certification

About

Xiaopeng Hong is a leading researcher in affective computing, multi-robot systems, and decentralized machine learning, with a particular focus on micro-expression recognition and intelligent automation. His most impactful work introduces a Spatio-Temporal Transformer that captures short- and long-range relations for micro-expression recognition—a breakthrough in detecting concealed emotions from fleeting, low-intensity facial cues (145 citations). In robotics, Hong addresses critical challenges in energy-efficient multi-robot task allocation under time window and precedence constraints, advancing practical deployment in warehouses and industrial environments. He has also pioneered deep class-incremental learning from decentralized data, a novel paradigm for handling continuous data streams across multiple repositories. His recent contributions span token-based deep reinforcement learning for vehicle routing problems, panoramic multi-object tracking via multimodality collaboration, and an empirical study on the Segment Anything model (SAM) for few-shot object counting. Through this diverse body of work, Hong bridges computer vision, reinforcement learning, and distributed systems, earning recognition for tackling real-world constraints—from fleeting emotional cues to complex logistical coordination.