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About

Xiaojing Tian is a robotics researcher whose work spans multi-robot systems, collaborative manipulation, and autonomous navigation. A central theme in Tian’s research is enabling robots to operate effectively in unstructured and unknown environments. For instance, their most cited work (10 citations) tackles the challenging problem of multi-robot raster map fusion without prior knowledge of relative positions, a critical capability for deploying robot teams in GPS-denied or disaster zones. Tian has also made notable contributions to service robotics, developing a dual-arm collaborative robot for clothing gripping and sorting to assist the elderly, and proposing a syntactic method for robot imitation learning of complex sequences, which enhances generalization beyond simple task repetition. Further work addresses high-precision collision detection under unknown loads, autonomous exploration and mapping, and novel mechanical design, including a deformable wheel-legged robot for improved mobility. With a strong focus on practical deployment, Tian’s research integrates perception, control, and mechanical innovation, demonstrating significant impact in advancing robot autonomy and human-robot collaboration.