198 citations · 235 across the 4 of their papers we have counts for
5 papers
Deep Model Predictive Optimization
Jacob Sacks, Rwik Rana, Kevin Huang +3
A major challenge in robotics is to design robust policies which enable complex and agile behaviors in the real world. On one end of the spectrum, we have model-free reinforcement…
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…
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…
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…
In-RDBMS Hardware Acceleration of Advanced Analytics
Divya Mahajan, Joon Kyung Kim, Jacob Sacks +3
The data revolution is fueled by advances in machine learning, databases, and hardware design. Programmable accelerators are making their way into each of these areas independently…