HiMEM：海馬に着想を得たVLAモデル向け記憶の統合・剪定・構成
HiMEM: Hippocampus-Inspired Memory Consolidation, Pruning, and Composition for Vision-Language-Action Models in 3D Scenes
3Dシーンで動くVLAモデルの記憶ノイズを減らすため、海馬に倣ってエピソード記憶を意味記憶へ統合し、冗長な記憶を剪定し、複数記憶を組み合わせる仕組みを提案した。LIBEROでpi_0の成功率を91.88%から98.20%に向上。
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著者: Boran Zhao, Yanbin Hu, Jun Ye, Zhenxian Hu, Yuxin Zhai, Jin Cui, Pengju Ren
分類: cs.CV, cs.AI
原文アブストラクト
Memory plays a crucial role in long-horizon task execution and cross-task generalization for Vision-Language-Action (VLA) models operating in complex 3D scenes. However, existing VLA memory mechanisms suffer from memory noise, particularly in complex 3D scenes, as stored episodic memories contain extensive redundant details about scene layouts, object configurations, and robot-specific states, which obscure generalizable manipulation principles and hinder cross-task transfer. To address this challenge, we first introduce a hippocampus-inspired memory consolidation mechanism that synthesizes extensive episodic experiences into a compact set of essential semantic memories, enabling VLA models to derive generalizable principles from practical experience. Second, we propose an episodic memory pruning mechanism that removes redundant episodic memories whose contents are captured by consolidated semantic memories, thereby increasing effective memory capacity and improving long-horizon task execution. Finally, to handle complex task scenarios, we develop a flexible memory composition mechanism that jointly leverages multiple episodic and semantic memories to enhance cross-scenario generalization. Experiments on LIBERO, LIBERO-Plus demonstrate the effectiveness of HiMEM across different VLA backbones, improving the average success rate of pi_0 from 91.88% to 98.20% on LIBERO.