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About

Josip Josifovski is a robotics and artificial intelligence researcher whose work sits at the intersection of robot perception, deep learning, and autonomous decision-making. His research spans visuo-haptic object perception, sim-to-real transfer, reinforcement learning, and robotic manipulation — areas where he has made meaningful contributions to bridging the gap between simulated training environments and real-world robotic deployment. His most cited work, "Visuo-haptic Object Perception for Robots: An Overview" (2023, 62 citations), provides a comprehensive examination of how robots can integrate vision and touch for human-like object understanding. Earlier, his 2018 paper on CNN-based object detection and pose estimation using synthetic training data (38 citations) tackled a critical challenge in computer vision for robotics. His 2021 work on reinforcement learning for gas source localization (29 citations) demonstrated the power of domain-knowledge-assisted autonomous navigation in hazardous environments. Josifovski has also pioneered techniques in domain randomization, contributing papers on continual domain randomization and sim2real analysis that address how robots can generalize learned behaviors to unpredictable real-world conditions. His early work on robotic home assistants with memory-aid functionality (2016) reflects a consistent dedication to socially relevant, human-centered robotics throughout his career.