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About

Pouria Tajvar is a robotics researcher whose work lies at the intersection of formal methods, data-driven control, and multi-robot systems. His research focuses on enabling autonomous robots to operate safely and robustly in complex, uncertain environments. Tajvar’s most cited work, “Adaptive heterogeneous multi-robot collaboration from formal task specifications” (2021, 15 citations), addresses the challenge of coordinating diverse robot teams to achieve high-level tasks defined by temporal logic specifications. He further advances safe autonomy through “Safe Data-Driven Model Predictive Control of Systems With Complex Dynamics” (2023, 14 citations), which tackles the critical problem of learning accurate models for systems with temporally or spatially varying dynamics while ensuring safety. His contributions extend to robust motion planning for non-holonomic robots under geometric constraints (2022, 7 citations) and exploration of unknown terrains using feedback motion primitives (2021, 3 citations). Notably, Tajvar has also explored data-driven damage detection and control adaptation for autonomous underwater vehicles (2022, 2 citations), demonstrating the breadth of his impact across both ground and underwater robotics. With a growing citation record, Tajvar is establishing himself as a key figure in the development of provably safe, adaptive autonomous systems.