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About

Masoumeh Mansouri is a robotics and AI researcher whose work spans multi-robot coordination, knowledge representation, hybrid planning, and the emerging field of cultural robotics. Her research addresses some of the most challenging problems in deploying autonomous robots in real-world environments, particularly the integration of symbolic reasoning with practical constraints such as time, space, and resources. Mansouri's most cited contribution, "A Loosely-Coupled Approach for Multi-Robot Coordination, Motion Planning and Control" (2018, 39 citations), tackles the complex interplay between planning and coordination in robot fleets — a critical bottleneck in industrial and service robotics. Her earlier work on ontology-based architectures for learning from experience (31 citations), developed within the EU-funded RACE project, demonstrated how robots can become more robust through structured knowledge frameworks. She has also pioneered stochastic approaches to multi-robot planning under uncertainty using Markov decision processes. More recently, Mansouri has made significant contributions to cultural robotics, critically examining how culture is defined and operationalized in robot design (26 and 14 citations), pushing for richer, more interdisciplinary understandings of culture beyond simple nationality-based frameworks. Her body of work, totalling hundreds of citations, reflects a researcher deeply committed to making autonomous robots both practically deployable and socially responsible.