長期視野VLAのための変分認知メモリ抽象化
Variational Cognitive Memory Abstraction for Long-Horizon Vision-Language-Action Models
VLAモデル向けに、軌道を圧縮した潜在メモリとして選択的に保持・再利用する変分認知メモリ機構を提案し、長期マニピュレーション性能を向上させた論文。
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著者: Xinyu Liu, Daiying Tian, Chenwei Tang, Ruyang Han, Chengyang Zhang, Qingyu Zeng, Wei Ju, Liangli Zhen, Jiancheng Lv
分類: cs.AI
原文アブストラクト
Vision-Language-Action (VLA) models have shown remarkable success in robotic manipulation, yet their ability to execute long-horizon tasks remains limited by insufficient long- term memory modeling. Existing methods either rely on implicit temporal representations within policy networks, where historical information can be gradually diluted, or maintain explicit memory buffers that suffer from redundancy, noise accumulation, and inefficient retrieval. In this paper, we propose CogMemory, a novel variational cognitive memory framework that enables VLA systems to selectively preserve and reuse task-relevant historical information. Our approach encodes multimodal trajectories into compact latent memory representations through a selective state- space memory encoder, multi-slot temporal abstraction, and variational information compression. During action generation, dynamic cross-attention retrieval with gated fusion adaptively incorporates relevant memories into policy prediction. With merely 3.45M additional parameters, CogMemory stays lightweight and seamlessly fits both early- and intermediate-fusion VLA backbones. Extensive experiments on long-horizon robotic manipulation benchmarks demonstrate consistent improvements across diverse VLA backbones.