The first systematic review of routine computing synthesizes literature into a taxonomy of temporal, behavioral, cognitive, and variability aspects, outlining applications in health, accessibility, and adaptive support along with persistent challenges.
Preiss and Wolfgang H\"onig and Gaurav S
3 Pith papers cite this work. Polarity classification is still indexing.
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MA-AC-MPC extends actor-critic MPC to multi-agent reinforcement learning and reports higher success rates than MLP baselines in pursuit-evasion simulation and hardware drone-rover landing.
An RBF neural network augments feedback linearization for quadrotors, adapts online without pre-training, guarantees asymptotic tracking via Lyapunov analysis, and reduces position and yaw RMSE versus baseline in Gazebo and Crazyflie experiments.
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Merging model-based control with multi-agent reinforcement learning for multi-agent cooperative teaming strategies
MA-AC-MPC extends actor-critic MPC to multi-agent reinforcement learning and reports higher success rates than MLP baselines in pursuit-evasion simulation and hardware drone-rover landing.
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Machine Learning-based Feedback Linearization Control of Quadrotor Subject to Unmodeled Dynamics
An RBF neural network augments feedback linearization for quadrotors, adapts online without pre-training, guarantees asymptotic tracking via Lyapunov analysis, and reduces position and yaw RMSE versus baseline in Gazebo and Crazyflie experiments.