Muhammad Fikko Fadjrimiratno
Papers
2
Total Citations
12
H-Index
2
About
Muhammad Fikko Fadjrimiratno is a researcher at the intersection of robotics, artificial intelligence, and anomaly detection, with a focus on enabling autonomous systems to understand and respond to human behavior. His work centers on developing intelligent mobile robots that can monitor environments and identify unusual activities in real time. Fadjrimiratno’s key contribution lies in integrating cognitive architectures inspired by dual-process theory—specifically, “fast and slow” thinking—into robotic perception, allowing machines to efficiently detect anomalies by combining rapid pattern recognition with deeper, more deliberate analysis. His most cited paper, "Detecting Anomalies from Human Activities by an Autonomous Mobile Robot based on 'Fast and Slow' Thinking" (2021, 8 citations), demonstrates this approach, while his earlier work, "Experimental Evaluation of GAN-Based One-Class Anomaly Detection on Office Monitoring" (2020, 4 citations), explores generative adversarial networks for robust, unsupervised anomaly detection in office settings. Though his citation counts are modest, Fadjrimiratno’s research is notable for its practical, experimental grounding and its innovative fusion of cognitive science and machine learning, offering a promising pathway toward safer, more perceptive autonomous systems in human-centric environments.
Research Focus
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Top Papers
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