Nathalie Majcherczyk

Worcester Polytechnic Institute, Université Libre de Bruxelles

Papers

Total Citations

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About

Nathalie Majcherczyk’s research lies at the intersection of multi-robot systems, decentralized control, and machine learning, with a focus on enabling swarms of robots to operate autonomously and intelligently in complex, real-world environments. Her most influential work, “Decentralized Connectivity-Preserving Deployment of Large-Scale Robot Swarms” (2018, 23 citations), introduces a scalable algorithm that allows robot teams to reach multiple targets while maintaining a connected communication network—a critical capability for search-and-rescue or environmental monitoring missions. In “Learning to View: Decision Transformers for Active Object Detection” (2023, 16 citations), she pioneers the use of transformer-based models to couple motion planning with perception, enabling robots to actively reposition themselves for improved object detection. More recently, her paper “Flow-FL: Data-Driven Federated Learning for Spatio-Temporal Predictions in Multi-Robot Systems” (2021, 5 citations) applies federated learning to distributed robot teams, allowing them to collaboratively learn from local data without centralizing sensitive information. Majcherczyk’s work is notable for bridging theory and practice, as demonstrated by her experimental characterization of the Drobot microrobotic platform. With a growing citation record and contributions spanning swarm deployment, active perception, and privacy-preserving learning, she is shaping the future of autonomous, cooperative robotics.