DART-VLN: 離散視覚言語ナビゲーションのためのテスト時メモリ減衰と反復防止正則化
DART-VLN: Test-Time Memory Decay and Anti-Loop Regularization for Discrete Vision-Language Navigation
メモリベースの離散VLNエージェントの推論時失敗を軽減するため、学習不要の推論時フレームワークを提案。古いメモリの重み付けを下げるメモリ減衰と、即座の逆戻りを防ぐ反復防止正則化を導入し、R2RとREVERIEで性能維持・向上と効率改善を実証した。
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著者: Shaoheng Zhang, Zhichen Li, Jie Mei
分類: cs.RO, cs.AI
原文アブストラクト
Memory-based agents for discrete vision-language navigation (VLN) operate under partial observability and can exhibit systematic inference-time failures even with strong pretrained backbones. We focus on two recurring problems: stale historical evidence during memory readout and inefficient local backtracking during action selection. We present DART-VLN, a training-free inference-time framework that combines Test-Time Memory Decay, which reweights stale and redundant memory slots without modifying their stored content, with Anti-Loop Regularization, a lightweight next-hop penalty that discourages immediate reversals. DART-VLN introduces no learnable parameters and leaves the navigation backbone unchanged. Experiments on R2R and REVERIE show that memory decay consistently preserves or improves task performance while reducing runtime. Adding anti-loop regularization further shortens trajectories, reduces local backtracking, and achieves the best overall balance between navigation quality and efficiency among the evaluated GridMM variants. These results indicate that lightweight inference-time control can improve the reliability and efficiency of memory-based discrete VLN without retraining.
