環境条件に応じたモデル予測制御の文脈的チューニング
Contextual Tuning of Model Predictive Control for Autonomous Racing
学習ベースのMPCで環境条件を文脈としてエンコードし、文脈的ベイズ最適化により雨天など異なる条件下でも少ないデータでモデルと目的関数を同時にチューニングする手法を提案し、RCカーで3000周以上走行して検証した。
著者: Lukas P. Fröhlich, Christian Küttel, Elena Arcari, Lukas Hewing, Melanie N. Zeilinger, Andrea Carron
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
Learning-based model predictive control has been widely applied in autonomous racing to improve the closed-loop behaviour of vehicles in a data-driven manner. When environmental conditions change, e.g., due to rain, often only the predictive model is adapted, but the controller parameters are kept constant. However, this can lead to suboptimal behaviour. In this paper, we address the problem of data-efficient controller tuning, adapting both the model and objective simultaneously. The key novelty of the proposed approach is that we leverage a learned dynamics model to encode the environmental condition as a so-called context. This insight allows us to employ contextual Bayesian optimization to efficiently transfer knowledge across different environmental conditions. Consequently, we require fewer data to find the optimal controller configuration for each context. The proposed framework is extensively evaluated with more than 3'000 laps driven on an experimental platform with 1:28 scale RC race cars. The results show that our approach successfully optimizes the lap time across different contexts requiring fewer data compared to other approaches based on standard Bayesian optimization.
