Robin Jaulmes

McGill University, Direction Générale de l'Armement

Papers

Total Citations

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About

Robin Jaulmes is a researcher whose work lies at the intersection of robotics, control theory, and artificial intelligence, with a primary focus on decision-making under uncertainty. His most influential contribution, the 2007 paper "A formal framework for robot learning and control under model uncertainty" (22 citations), extends the foundational partially observable Markov decision process (POMDP) framework to address a critical real-world challenge: robots operating with incomplete or changing models of their environment. This work provides a rigorous mathematical structure for learning and adapting control policies when the environment's dynamics are not fully known, a key step toward truly autonomous systems. Jaulmes also contributed to practical robotics architectures, as seen in his work on the HNG robust architecture for mobile robots (2008), and developed methodologies for assessing robot autonomous functionalities (2007). While his citation counts reflect a focused, technically deep body of work rather than broad popularity, his formalization of model uncertainty in POMDPs remains a valuable reference for researchers tackling the fundamental problem of how robots can learn and act reliably in the unpredictable real world.