Max Robotics — Global Service Robot Directory + US Certification

About

Hajime Murao is a Japanese researcher whose work sits at the intersection of reinforcement learning, robotics, and intelligent systems design. He is best known for his contributions to adaptive state space construction in Q-learning, most notably through his development of QLASS (Q-Learning with Adaptive State Space Segmentation), which addressed one of the central challenges in applying reinforcement learning to real-world robotic tasks — constructing suitable state representations without prior domain knowledge. This work, his most cited with 22 citations, laid important groundwork for practical reinforcement learning deployments. Murao's broader research agenda encompasses multi-robot scheduling using genetic algorithms, modular reinforcement learning for quadruped locomotion, and techniques for handling Partially Observable Markov Decision Processes (POMDPs) through innovative state space filtering methods. His 2012 paper on co-constructing continuous high-dimensional state and action spaces reflects his sustained commitment to pushing reinforcement learning toward increasingly complex, real-world environments. Beyond robotics, Murao has contributed to the theoretical foundations of design methodology, formalizing the design process as an inverse problem and exploring emergent computation. Across more than a decade of publications, his research consistently advances the goal of enabling machines to learn adaptive, autonomous behavior with minimal human supervision — a challenge of enduring relevance to modern artificial intelligence.