制御認識型拡張による汎用視覚運動方策の効率的学習
Efficient Training of Generalizable Visuomotor Policies via Control-Aware Augmentation
自己教師ありの制御関連領域マスクで拡張を適用し、専門家から生徒方策へ蒸留することで、未知環境でも微調整なしに汎化する視覚運動方策を効率的に学習するフレームワークEAGLEを提案。
著者: Yinuo Zhao, Kun Wu, Tianjiao Yi, Zhiyuan Xu, Xiaozhu Ju, Zhengping Che, Chi Harold Liu, Jian Tang
分類: cs.RO, cs.CV
原文アブストラクト
Improving generalization is one key challenge in embodied AI, where obtaining large-scale datasets across diverse scenarios is costly. Traditional weak augmentations, such as cropping and flipping, are insufficient for improving a model's performance in new environments. Existing data augmentation methods often disrupt task-relevant information in images, potentially degrading performance. To overcome these challenges, we introduce EAGLE, an efficient training framework for generalizable visuomotor policies that improves upon existing methods by (1) enhancing generalization by applying augmentation only to control-related regions identified through a self-supervised control-aware mask and (2) improving training stability and efficiency by distilling knowledge from an expert to a visuomotor student policy, which is then deployed to unseen environments without further fine-tuning. Comprehensive experiments on three domains, including the DMControl Generalization Benchmark, the enhanced Robot Manipulation Distraction Benchmark, and a long-sequential drawer-opening task, validate the effectiveness of our method.
