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logistics, inspection, rescue in disaster areas and precision agriculture. The originality of the MMR project lies in the synergistic integration of robust predictive control (Robust/Stochastic MPC
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protocols, optimizations, and tools to protect data and functions in applications. For this, we use and combine Multi-Party Computation (MPC), Private Function Evaluation (PFE), and Private Set Operations
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-input multi-output (MIMO) closed-loop controllers (MPC, RL, hybrid RL-MPC), and real-time actuator management architectures embedded in MATLAB/Simulink digital-twin environments (COTSIM). This role offers
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robotics – reinforcement learning, whole-body model predictive control (MPC), and differentiable optimal control – to simulate human balance and step recovery in urban transport scenarios. The goal is a