Papers
3
Total Citations
86
H-Index
3
About
Ziyang Zhang is a rising researcher at the intersection of robotics, human–machine interaction, and artificial intelligence. His work centers on developing intelligent systems that are both perceptually robust and socially attuned. Zhang’s most cited paper, “Deep Reinforcement Learning for Robot Collision Avoidance With Self-State-Attention and Sensor Fusion” (2022, 59 citations), introduces a novel deep reinforcement learning framework that fuses 3D LiDAR data with self-state-attention mechanisms, enabling robots to navigate complex environments with greater safety and efficiency—a significant advance over traditional 2D LiDAR-based systems. He also explores the human side of automation: in “Exploring user experience and performance of a tedious task through human–agent relationship” (2023, 12 citations), Zhang demonstrates that positive human-agent relationships can measurably improve task performance and user satisfaction. His recent work on noise-tolerant human–machine interfaces (2025, 15 citations) further underscores his commitment to practical, resilient systems. With a growing citation record and contributions spanning sensor fusion, reinforcement learning, and human-robot interaction, Ziyang Zhang is shaping the future of autonomous systems that work not only effectively but harmoniously alongside people.
Research Focus
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