Bodi Yuan

Xi’an University of Posts and Telecommunications, Science Factory

Papers

Total Citations

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About

Bodi Yuan is a robotics researcher advancing intelligent manipulation and multi-agent coordination. His work centers on safe human-robot interaction, multi-robot path planning, and control theory, with a focus on enabling robots to operate effectively in cluttered, dynamic environments. In his widely cited 2024 paper "Actor-Hybrid-Attention-Critic for Multi-Logistic Robots Path Planning" (9 citations), Yuan introduced a novel deep reinforcement learning framework that uses hybrid attention mechanisms to help multiple logistic robots extract critical information from complex static and dynamic scenes—a key challenge for the growing autonomous delivery sector. His 2023 work "Allowing Safe Contact in Robotic Goal-Reaching" (4 citations) tackles the underexplored problem of permitting intentional, safe contact between robot bodies and obstacles during manipulation, drawing inspiration from human daily movements. By planning and tracking in both operational and null spaces, Yuan’s approach bridges the gap between collision-free ideals and practical, contact-rich tasks. His research has direct implications for warehouse automation, assistive robotics, and domestic service robots, where safe physical interaction is essential. Yuan’s contributions are shaping the next generation of robots that can work alongside humans—and their environments—with greater adaptability and safety.