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Optimal control is often used in robotics for planning a trajectory toachieve some desired behavior, as expressed by the cost function. Most works inoptimal control focus on finding a single optimal trajectory, which is thentypically tracked by another controller. In this work, we instead considertrajectory distribution as the solution of an optimal control problem,resulting in better tracking performance and a more stable controller. AGaussian distribution is first obtained from an iterative Linear QuadraticRegulator (iLQR) solver. A short horizon Model Predictive Control (MPC) is thenused to track this distribution. We show that tracking the distribution is morecost-efficient and robust as compared to tracking the mean or using iLQRfeedback control. The proposed method is validated with kinematic control of7-DoF Panda manipulator and dynamic control of 6-DoF quadcopter in simulation.

Probabilistic Iterative LQR for Short Time Horizon MPC