Model Predictive Path Integral (MPPI) is a recognized sampling-based approach for finite
horizon optimal control problems. However, the efficacy and computational efficiency of …

In this paper, we propose the Model Reference Adaptive Control & Reinforcement Learning
(MRAC-RL) approach to developing online policies for systems in which modeling errors …

The classical Model Predictive Path Integral (MPPI) control framework lacks reliable safety
guarantees since it relies on a risk-neutral trajectory evaluation technique, which can …

Model predictive control (MPC) is a powerful technique for solving dynamic control tasks. In
this paper, we show that there exists a close connection between MPC and online learning …

Although state-feedback optimal control of the continuous-time (CT) systems has been
extensively studied, resolving optimal control online via output-feedback is still challenging …

Model Predictive Path Integral (MPPI) control is a type of sampling-based model predictive
control that simulates thousands of trajectories and uses these trajectories to synthesize …

Control design for robotic systems is complex and often requires solving an optimization to
follow a trajectory accurately. Online optimization approaches like Model Predictive Control …

We generalize the derivation of model predictive path integral control (MPPI) to allow for a
single joint distribution across controls in the control sequence. This reformation allows for …

Traditional automated vehicle path-tracking algorithms require plant models to derive the
respective control laws. However, the accurate vehicle model is difficult to obtain due to the …

We present a sampling-based control approach that can generate smooth actions for
general nonlinear systems without external smoothing algorithms. Model Predictive Path …