Real‐Time Sampling‐Based Model Predictive Control Based on Reverse Kullback–Leibler Divergence and Its Adaptive Acceleration

Sampling‐based model predictive control (MPC) has the potential for use in a wide variety of robotic systems. However, its unstable updates and poor convergence render it unsuitable for real‐time control of robotic systems. This study addresses this challenge with a novel approach from reverse Kullback–Leibler divergence, which has a mode‐seeking property and is likely to find one of the locally optimal solutions early. Using this approach, a weighted maximum likelihood estimation with positive and negative weights is obtained and solved using the mirror descent (MD) algorithm. Negative weights eliminate unnecessary actions, but a practical implementation needs to be designed to avoid interference with…

• JS
  Japan Science and Technology Corporation

  Awards: JPMJPR20C3, JPMJCS24K6
• PR
  Precursory Research for Embryonic Science and Technology

  Award: JPMJPR20C3