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This work introduces DuoMind, a distributed hierarchical framework for multi-robot coordination through semantic communication, and develops RoboPoly, a benchmark comprising long-horizon manipulation tasks that require coordinated, closed-loop execution under distributed control.
A convolutional neural network architecture is developed to learn spatio-temporal patterns from multivariate flight dynamics, enabling direct inference of both the faulty rotor and its damage level and supporting real-time health monitoring for autonomous VTOL systems.
Results indicate that the correct learned policy can outperform classical vision and control robotic methods in robustness, generalization, and deployment efficiency, justifying a shift toward scalable robotic automation for high up-time industrial environments.
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