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About

Riku Murai is a robotics researcher whose work spans two interconnected frontiers: intelligent path planning and ultra-efficient focal-plane vision processing. He is perhaps best known for developing PathBench, a benchmarking platform that systematically evaluates both classical and learning-based path planning algorithms under a unified interface — a contribution that has attracted nearly 40 citations across its publications and addressed a longstanding gap in mobile robotics methodology. His subsequent systematic comparison study further solidified PathBench as a practical tool for the robotics community navigating the transition from traditional algorithms like RRT and wavefront planners toward deep learning-based approaches. Alongside this, Murai has pioneered the application of Focal-Plane Sensor-Processors (FPSPs) to robotic perception, demonstrating that these highly resource-constrained, pixel-level computing devices can support demanding tasks including CNN inference, visual odometry, and high-frame-rate homography estimation. His BIT-VIO system exemplifies this vision, fusing focal-plane binary features with inertial data to achieve agile, low-power navigation. Collectively, Murai's research addresses a fundamental challenge in robotics: enabling fast, efficient scene understanding without sacrificing computational practicality — making his work highly relevant to the emerging field of edge robotics.