MAVEN: 動的変化に強いアジャイルクアッドローター操縦のためのメタ強化学習フレームワーク
MAVEN: A Meta-Reinforcement Learning Framework for Varying-Dynamics Expertise in Agile Quadrotor Maneuvers
クアッドローターの動的特性が大きく変わる状況でも単一のポリシーで頑健にナビゲーションできるよう、予測的コンテキストエンコーダを備えたメタ強化学習フレームワークMAVENを提案し、質量変化や単一ローター推力喪失下での適応性と実機へのゼロショット転送を実証した。
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著者: Jin Zhou, Dongcheng Cao, Xian Wang, Shuo Li
分類: cs.RO
原文アブストラクト
Reinforcement learning (RL) has emerged as a powerful paradigm for achieving online agile navigation with quadrotors. Despite this success, policies trained via standard RL typically fail to generalize across significant dynamic variations, exhibiting a critical lack of adaptability. This work introduces MAVEN, a meta-RL framework that enables a single policy to achieve robust end-to-end navigation across a wide range of quadrotor dynamics. Our approach features a novel predictive context encoder, which learns to infer a latent representation of the system dynamics from interaction history. We demonstrate our method in agile waypoint traversal tasks under two challenging scenarios: large variations in quadrotor mass and severe single-rotor thrust loss. We leverage a GPU-vectorized simulator to distribute tasks across thousands of parallel environments, overcoming the long training times of meta-RL to converge in less than an hour. Through extensive experiments in both simulation and the real world, we validate that MAVEN achieves superior adaptation and agility. The policy successfully executes zero-shot sim-to-real transfer, demonstrating robust online adaptation by performing high-speed maneuvers despite mass variations of up to 66.7% and single-rotor thrust losses as severe as 70%.
