  Neural networks are lately more and more often being used in the context of
data-driven control, as an approximate model of the true system dynamics. Model
Predictive Control (MPC) adopts this practise leading to neural MPC strategies.
This raises a question of whether the trained neural network has converged and
generalized in a way that the learned model encapsulates an accurate
approximation of the true dynamic model of the system, thus making it a
reliable choice for model-based control, especially for disturbed and uncertain
systems. To tackle that, we propose Dropout MPC, a novel sampling-based
ensemble neural MPC algorithm that employs the Monte-Carlo dropout technique on
the learned system model. The closed loop is based on an ensemble of predictive
controllers, that are used simultaneously at each time-step for trajectory
optimization. Each member of the ensemble influences the control input, based
on a weighted voting scheme, thus by employing different realizations of the
learned system dynamics, neural control becomes more reliable by design. An
additional strength of the method is that it offers by design a way to estimate
future uncertainty, leading to cautious control. While the method aims in
general at uncertain systems with complex dynamics, where models derived from
first principles are hard to infer, to showcase the application we utilize data
gathered in the laboratory from a real mobile manipulator and employ the
proposed algorithm for the navigation of the robot in simulation.
