2 citations · 2 across the 1 of their papers we have counts for
3 papers
cs.RO2022★ 2 cited
Learning Sampling Distributions for Model Predictive Control
Jacob Sacks, Byron Boots
Sampling-based methods have become a cornerstone of contemporary approaches to Model Predictive Control (MPC), as they make no restrictions on the differentiability of the dynamics…
cs.RO2019
An Online Learning Approach to Model Predictive Control
Nolan Wagener, Ching-An Cheng, Jacob Sacks +1
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 learnin…
cs.LG2018
Differentiable MPC for End-to-end Planning and Control
Brandon Amos, Ivan Dario Jimenez Rodriguez, Jacob Sacks +2
We present foundations for using Model Predictive Control (MPC) as a differentiable policy class for reinforcement learning in continuous state and action spaces. This provides one…