ReMem-VLA: 二重レベル再帰クエリによる記憶を備えた視覚言語行動モデル
ReMem-VLA: Empowering Vision-Language-Action Model with Memory via Dual-Level Recurrent Queries
視覚言語行動モデルに、フレーム単位とチャンク単位の再帰クエリを導入し、短期・長期記憶を実現。過去の観測予測を補助タスクとして追加し、記憶依存タスクで既存モデルを大幅に上回る性能を達成した。
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著者: Hang Li, Fengyi Shen, Dong Chen, Liudi Yang, Xudong Wang, Jinkui Shi, Zhenshan Bing, Ziyuan Liu, Alois Knoll
分類: cs.RO
原文アブストラクト
Vision-language-action (VLA) models for closed-loop robot control are typically cast under the Markov assumption, making them prone to errors on tasks requiring historical context. To incorporate memory, existing VLAs either retrieve from a memory bank, which can be misled by distractors, or extend the frame window, whose fixed horizon still limits long-term retention. In this paper, we introduce ReMem-VLA, a Recurrent Memory VLA model equipped with two sets of learnable queries: frame-level recurrent memory queries for propagating information across consecutive frames to support short-term memory, and chunk-level recurrent memory queries for carrying context across temporal chunks for long-term memory. These queries are trained end-to-end to aggregate and maintain relevant context over time, implicitly guiding the model's decisions without additional training or inference cost. Furthermore, to enhance visual memory, we introduce Past Observation Prediction as an auxiliary training objective. Through extensive memory-centric simulation and real-world robot experiments, we demonstrate that ReMem-VLA exhibits strong memory capabilities across multiple dimensions, including spatial, sequential, episodic, temporal, and visual memory. ReMem-VLA significantly outperforms memory-free VLA baselines $\pi$0.5 and OpenVLA-OFT and surpasses MemoryVLA on memory-dependent tasks by a large margin.
