Papers
2
Total Citations
25
H-Index
2
About
Pengzhi Yang is an emerging researcher specializing in autonomous underwater robotics, active perception, and learning-based control systems. His work sits at the intersection of computer vision, information theory, and robotic navigation, with a particular focus on enabling robots to operate intelligently in challenging, real-world environments. Among his most notable contributions is a pioneering approach to underwater collision-free navigation that fuses monocular camera imagery with single-beam sonar data, leveraging domain randomization to bridge the gap between simulation and real-world deployment — a critical challenge in underwater robotics where data collection is inherently difficult. This work has already garnered 14 citations since its 2023 publication, signaling strong interest from the robotics community. Equally impactful is his research on learning continuous control policies for information-theoretic active perception, which addresses how mobile robots can autonomously maximize information gain during landmark localization tasks. By framing exploration as a mutual information maximization problem, Yang advances the frontier of intelligent, goal-directed robot behavior. With 11 citations since 2023, this work reflects growing recognition of his contributions. Yang's research portfolio suggests a researcher poised to make lasting contributions to autonomous systems, particularly in perception-limited and unstructured environments.
Research Focus
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Top Papers
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