VLA-Pro: 視覚言語行動モデルのためのクロスタスク手続き記憶転送
VLA-Pro: Cross-Task Procedural Memory Transfer for Vision-Language-Action Models
タスク固有のLoRAアダプタを手続き記憶として保存し、推論時に現在のマルチモーダル文脈に基づいて関連記憶を検索・融合することで、未見タスクへの汎化を向上させるプラグアンドプレイフレームワークを提案した。
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著者: Shengyu Si, Yuanzhuo Lu, Ruimeng Yang, Ziyi Ye, Zuxuan Wu, Yu-Gang Jiang
分類: cs.RO, cs.AI, cs.CV
原文アブストラクト
Vision-Language-Action~(VLA) models have shown strong potential for general-purpose robotic manipulation, yet they still struggle to generalize to unseen tasks that necessitate transferring relevant experience across objects, scenes, and action patterns. This paper proposes VLA-Pro, a plug-and-play framework designed to enhance cross-task generalization by storing task-relevant procedural memories at training time and transferring these memories during inference. Specifically, VLA-Pro stores task-specific LoRA adapters as parameterized procedural memories during training. At inference time, VLA-Pro retrieves relevant procedural memories based on the current multi-modal context and dynamically fuses these memories for generating the current action chunk. Experiments on RoboTwin, RLBench, and real-world manipulation tasks show that VLA-Pro consistently improves cross-task generalization across multiple backbones, achieving up to a 207% relative improvement in simulation and increasing real-world success rate from 5.8% to 65.0%. These results suggest that procedural memory retrieval and adaptation provide an effective mechanism for transferring manipulation experience to novel tasks while preserving modularity and execution stability.
