Shuze Wang

Huazhong University of Science and Technology, Beijing Institute of Technology

Papers

Total Citations

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About

Shuze Wang is a researcher at the forefront of human-robot collaboration (HRC) and embodied AI, with a focus on enabling robots to understand and anticipate human behavior in shared workspaces. Their key contributions lie in three interconnected areas: human motion prediction, robotic manipulation, and 6-DoF object pose tracking. Wang’s seminal work on human motion trajectory prediction in HRC tasks introduced a Gaussian Process-based framework to model human-robot distances, directly improving safety and efficiency in collaborative handover operations—a foundational paper that has garnered 4 citations. More recently, Wang has advanced world models for robot arm grasping by incorporating backward dynamics prediction (3 citations), allowing robots to learn from past actions to refine future grasps. In the domain of perception, Wang’s multi-modal approach to 6-DoF object pose tracking (2 citations) fuses spatial cues with monocular RGB imagery, achieving robust performance without depth sensors. This work is particularly notable for its potential in real-world applications where cost and sensor limitations are critical. Wang’s research is steadily building a citation footprint, reflecting a growing influence in the robotics community.