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About

Muhannad Alomari is a robotics and artificial intelligence researcher whose work spans long-term robot autonomy, natural language grounding, qualitative spatial reasoning, and unsupervised activity recognition. He is perhaps best known for his contributions to the STRANDS Project, a landmark initiative exploring extended autonomous robot operation in real-world environments, which has garnered nearly 200 citations and remains a touchstone for service robotics research. Alomari has made significant strides in enabling robots to acquire and ground natural language commands through cognitively inspired frameworks that link visual semantics with grammar induction — work reflected across several well-cited papers from 2017. His development of QSRlib, a software library for extracting qualitative spatial relations from video, has proven a practical and widely adopted tool for the activity analysis community. Further contributions include unsupervised activity recognition aboard mobile robots using techniques such as Latent Dirichlet Allocation, and early work on collision-free path planning using continuous genetic algorithms. His 2021 system, OLAV, synthesizes many of these threads by achieving simultaneous online perceptual and language learning. Collectively, Alomari's research advances the vision of robots that can perceive, communicate, and adapt intelligently within human environments.