部分自動化下での外乱注入：長期的タスクのためのロバスト模倣学習
Disturbance Injection under Partial Automation: Robust Imitation Learning for Long-horizon Tasks
部分自動化環境で、人間の操作とモード切替を模倣学習し、外乱注入でロバスト性を高める手法を提案。長期的タスクで有効性を検証。
著者: Hirotaka Tahara, Hikaru Sasaki, Hanbit Oh, Edgar Anarossi, Takamitsu Matsubara
分類: cs.RO, cs.LG
原文アブストラクト
Partial Automation (PA) with intelligent support systems has been introduced in industrial machinery and advanced automobiles to reduce the burden of long hours of human operation. Under PA, operators perform manual operations (providing actions) and operations that switch to automatic/manual mode (mode-switching). Since PA reduces the total duration of manual operation, these two action and mode-switching operations can be replicated by imitation learning with high sample efficiency. To this end, this paper proposes Disturbance Injection under Partial Automation (DIPA) as a novel imitation learning framework. In DIPA, mode and actions (in the manual mode) are assumed to be observables in each state and are used to learn both action and mode-switching policies. The above learning is robustified by injecting disturbances into the operator's actions to optimize the disturbance's level for minimizing the covariate shift under PA. We experimentally validated the effectiveness of our method for long-horizon tasks in two simulations and a real robot environment and confirmed that our method outperformed the previous methods and reduced the demonstration burden.
