Zhizhuo Yang
Papers
1
Total Citations
2
H-Index
1
About
Zhizhuo Yang is a rising researcher at the intersection of robotics, artificial intelligence, and cognitive science, whose work focuses on developing autonomous agents capable of learning and acting under uncertainty. His primary research areas include active inference, world models, and reinforcement learning, with a particular emphasis on solving sparse-reward tasks from high-dimensional sensory inputs like pixels. Yang's major contribution is the development of SR-AIF, a novel framework that extends active inference to partially observable Markov decision processes (POMDPs), enabling robots to efficiently explore and achieve goals in complex, real-world environments where rewards are rare. This work, published in 2025, has already garnered early citations, signaling its potential to influence future research in model-based AI and robotics. By bridging theoretical principles of active inference with practical deep learning architectures, Yang is helping to pave the way for more sample-efficient and interpretable robotic systems. His research holds promise for advancing embodied AI, particularly in domains requiring long-horizon planning and robust perception under uncertainty.
Research Focus
Key Achievements
Top Papers
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