バイアス付きMPPI：補助コントローラを融合したサンプリングベースモデル予測制御
Biased-MPPI: Informing Sampling-Based Model Predictive Control by Fusing Ancillary Controllers
サンプリングベースMPCの一種であるMPPIにおいて、任意のサンプリング分布を許容し、古典的および学習ベースの補助コントローラを同時に融合する重要度サンプリング手法を提案し、効率・ロバスト性・収束性を向上させた。
著者: Elia Trevisan, Javier Alonso-Mora
分類: cs.RO
原文アブストラクト
Motion planning for autonomous robots in dynamic environments poses numerous challenges due to uncertainties in the robot's dynamics and interaction with other agents. Sampling-based MPC approaches, such as Model Predictive Path Integral (MPPI) control, have shown promise in addressing these complex motion planning problems. However, the performance of MPPI relies heavily on the choice of sampling distribution. Existing literature often uses the previously computed input sequence as the mean of a Gaussian distribution for sampling, leading to potential failures and local minima. In this paper, we propose a novel derivation of MPPI that allows for arbitrary sampling distributions to enhance efficiency, robustness, and convergence while alleviating the problem of local minima. We present an efficient importance sampling scheme that combines classical and learning-based ancillary controllers simultaneously, resulting in more informative sampling and control fusion. Several simulated and real-world demonstrate the validity of our approach.
