MOON: 視覚言語ナビゲーションにおける忘却のタイミングと復帰行動の学習
MOON: Learning When to Forget and How to Move On in Vision-and-Language Navigation
凍結したVLNポリシーに逸脱判定とGRPOで訓練した復帰ポリシーの2つの軽量モジュールを追加し、経路逸脱時に履歴を消去して復帰を学習させることでR2R-CEの成功率を5.2ポイント向上させた論文。
著者: Jingmeng Zhou, Seson Sun, Binyao Xu, Lingming Zhang, Hanshi Wang, Fudong Ge, Weiming Hu, Jin Gao, Zhipeng Zhang
分類: cs.RO
原文アブストラクト
Recent vision-and-language navigation (VLN) policies act on their visual history, and an incorrect action can take them off the instructed route. Paired interventions on four VLN policies show that clearing the history doubles pooled success at the onset of a deviation but lowers it on route, while later in a deviation, removing only the off-route frames helps much less. After a deviation, the retained history keeps the policy from turning where a turn is needed, which we call behavioral inertia. We propose MOON, a framework that equips a frozen VLN policy with two lightweight modules, adding less than 1% of its parameters. A deviation judge decides when to forget, raising an alarm that clears the history when it predicts that the agent is off route. A recovery policy, trained with GRPO from alarm states, learns how to move on, turning more of the successes found only by sampling into a single greedy rollout. Experiments on R2R-CE and RxR-CE show that MOON reaches the highest R2R-CE success rate among the compared methods and raises the success rate of its frozen base by 5.2 and 6.0 points, and an ablation with a random trigger indicates that this gain depends on when the history is cleared. Without retraining, MOON also improves zero-shot object-goal navigation on HM3D-OVON. Together, these results suggest that deviation recovery depends on knowing when to forget, not only how to act. Code is available at https://github.com/AutoLab-SAI-SJTU/MOON.