ロボット学習における敵対的ロバスト性と精度のトレードオフ再考
Revisiting the Adversarial Robustness-Accuracy Tradeoff in Robot Learning
自動運転・移動ロボットナビゲーション・ジェスチャ認識の3タスクで敵対的学習のロバスト性と精度のトレードオフを体系的に評価し、最新手法でも精度低下がロバスト性向上を桁違いに上回ることを示した。
著者: Mathias Lechner, Alexander Amini, Daniela Rus, Thomas A. Henzinger
分類: cs.RO, cs.CV, cs.LG
原文アブストラクト
Adversarial training (i.e., training on adversarially perturbed input data) is a well-studied method for making neural networks robust to potential adversarial attacks during inference. However, the improved robustness does not come for free but rather is accompanied by a decrease in overall model accuracy and performance. Recent work has shown that, in practical robot learning applications, the effects of adversarial training do not pose a fair trade-off but inflict a net loss when measured in holistic robot performance. This work revisits the robustness-accuracy trade-off in robot learning by systematically analyzing if recent advances in robust training methods and theory in conjunction with adversarial robot learning, are capable of making adversarial training suitable for real-world robot applications. We evaluate three different robot learning tasks ranging from autonomous driving in a high-fidelity environment amenable to sim-to-real deployment to mobile robot navigation and gesture recognition. Our results demonstrate that, while these techniques make incremental improvements on the trade-off on a relative scale, the negative impact on the nominal accuracy caused by adversarial training still outweighs the improved robustness by an order of magnitude. We conclude that although progress is happening, further advances in robust learning methods are necessary before they can benefit robot learning tasks in practice.
