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About

Changbo Wang is a researcher whose work sits at the intersection of robotic manipulation, computer graphics, and human-robot interaction. His primary research areas include grasp planning, motion planning in cluttered environments, and the generation of expressive, emotion-aware gestures for virtual avatars. Wang’s major contributions include a novel approach for transferring grasp configurations from known objects to novel ones using active learning and local replanning, a method that has garnered 23 citations and demonstrates significant practical utility. He also developed an incremental sampling-based planner for retrieving near-cylindrical objects in cluttered scenes, which uses hierarchical graphs to efficiently compute obstacle-removal and collision-free motion plans (13 citations). Beyond manipulation, Wang has explored global penetration depth computation for articulated models and, most recently, generative data augmentation for face anti-spoofing that preserves liveness information. His 2025 work, “EmoDiffGes,” introduces a progressive synergistic diffusion model for emotion-aware co-speech holistic gesture generation, addressing a critical gap in producing expressive, synchronized bodily movements for human-robot interaction. With a growing citation record and a portfolio spanning foundational robotics to affective computing, Wang is shaping how robots perceive, plan, and interact with both objects and people.