Sampling Based Model Predictive Control

Sampling-based Model Predictive Control (MPC) is a control strategy that uses random sampling to optimize control actions over a prediction horizon, addressing challenges posed by high-dimensional or non-convex control problems. Current research focuses on improving the efficiency and robustness of these methods, particularly through novel sampling distributions (e.g., diffusion-style annealing, log-normal mixtures) and the integration of learned models or ancillary controllers to guide the sampling process. This approach shows promise in diverse applications, including robotic surgery, legged locomotion, and autonomous navigation in complex environments, offering a powerful alternative to traditional optimization-based MPC methods, especially in scenarios with uncertainty or high dimensionality.