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About

Xiaowen Chu is a researcher whose work spans robotics, swarm intelligence, computer vision, and edge computing — bridging foundational algorithmic challenges with practical autonomous systems. His most recognized contributions lie in the coordination and control of multi-robot systems: his 2018 study on self-adaptive collective motion of swarm robots (71 citations) addressed the underexplored problem of maintaining swarm cohesion along preplanned paths, while his earlier work on bi-connectivity in robotic sensor networks (62 citations) provided elegant movement control algorithms enabling robots to autonomously optimize sensing coverage. These papers established Chu as a key voice in swarm robotics and networked robotic systems. Chu has also made substantial contributions to indoor robotics perception through the development of IRS, a large-scale stereo dataset designed to train deep learning models for disparity and surface normal estimation. Released in successive iterations and accumulating over 57 citations across versions, IRS has become a meaningful benchmark resource for the indoor robotics vision community. His more recent work on fine-grained 6-DoF grasp detection reflects a growing focus on dexterous robotic manipulation. Complementing his technical research, Chu has contributed to the broader computing community through editorial leadership in mobile, edge, and cloud computing.