俊敏なクアッドロータ飛行のための学習制御ポリシーのベンチマーク比較
A Benchmark Comparison of Learned Control Policies for Agile Quadrotor Flight
俊敏なクアッドロータ飛行向けの学習制御ポリシーを比較し、機体レートと推力指令型がロータ推力直接指定型よりsim2real転移に頑健であることを示し、実機で45km/h超の飛行制御を初めて実現した。
著者: Elia Kaufmann, Leonard Bauersfeld, Davide Scaramuzza
分類: cs.RO
原文アブストラクト
Quadrotors are highly nonlinear dynamical systems that require carefully tuned controllers to be pushed to their physical limits. Recently, learning-based control policies have been proposed for quadrotors, as they would potentially allow learning direct mappings from high-dimensional raw sensory observations to actions. Due to sample inefficiency, training such learned controllers on the real platform is impractical or even impossible. Training in simulation is attractive but requires to transfer policies between domains, which demands trained policies to be robust to such domain gap. In this work, we make two contributions: (i) we perform the first benchmark comparison of existing learned control policies for agile quadrotor flight and show that training a control policy that commands body-rates and thrust results in more robust sim-to-real transfer compared to a policy that directly specifies individual rotor thrusts, (ii) we demonstrate for the first time that such a control policy trained via deep reinforcement learning can control a quadrotor in real-world experiments at speeds over 45km/h.
