VLAモデルの性能はどれだけkNN検索で説明できるか
How Much of Your VLA Is kNN
VLAモデルのLIBEROでの高成功率の多くが、訓練デモの近傍検索と行動チャンク再利用で再現できることを示し、検索キー品質を測る指標LACを提案した論文。
著者: Denis Grachev, Damien LaRocque, Omar Moured
分類: cs.RO, cs.AI, cs.LG
原文アブストラクト
Vision-language-action (VLA) models now exceed 95% success on the LIBERO simulation benchmark, yet it remains unclear how much of this performance requires generalization beyond the training demonstrations. In this work, we show that retrieving nearby demonstrations and reusing their action chunks achieves a success rate of 91.7 % on LIBERO without policy training. Under LIBERO-Plus perturbations, retrieval shows the smallest drop of all evaluated policies. Across nine publicly available VLAs, fine-tuning mostly makes the vision-language backbone a better retrieval key, and the actions of the VLAs remain close to the demonstrations. In addition, we introduce Local Action Consistency (LAC), an offline metric that measures whether nearby observations in representation space are also associated with similar future actions. We show that LAC tracks the success rate of retrieval and can guide the training of a retrieval key. Our evaluation and source code are available at https://github.com/sereact/lerobot_knn.