EchoVLA: 宣言的記憶を統合した移動マニピュレーション向け視覚-言語-行動モデル
EchoVLA: Robotic Vision-Language-Action Model with Synergistic Declarative Memory for Mobile Manipulation
人間の脳に着想を得たシーン記憶とエピソード記憶を組み合わせ、移動と操作を協調させるメモリ対応VLAモデルを提案し、大規模学習用ベンチマークMoManiも構築した。
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著者: Min Lin, Xiwen Liang, Bingqian Lin, Jingzhi Liu, Zijian Jiao, Kehan Li, Ziang Yan, Yu Sun, Weijia Liufu, Yuhan Ma, Jiarui Hu, Yuecheng Liu, Shen Zhao, Yuzheng Zhuang, Xiaodan Liang
分類: cs.RO
原文アブストラクト
Recent progress in Vision-Language-Action (VLA) models has enabled embodied agents to interpret multimodal instructions and perform complex tasks. However, existing VLAs are mostly confined to short-horizon, table-top manipulation, lacking the memory and reasoning capability required for mobile manipulation, where agents must coordinate navigation and manipulation under changing spatial contexts. In this work, we present EchoVLA, a memory-aware VLA model for mobile manipulation. EchoVLA incorporates a synergistic declarative memory inspired by the human brain, consisting of a scene memory that maintains a collection of spatial-semantic maps and an episodic memory that stores task-level experiences with multimodal contextual features. The two memories are individually stored, updated, and retrieved based on current observations, task history, and instructions, and their retrieved representations are fused via coarse- and fine-grained attention to guide base-arm diffusion policies. To support large-scale training, we further introduce MoMani, an automated benchmark that generates expert-level trajectories through multimodal large language model (MLLM)-guided planning and feedback-driven refinement, supplemented with real-robot demonstrations. Comprehensive simulated and real-world results demonstrate that EchoVLA substantially improves overall performance, e.g., it achieves the highest success rates of 0.52 on manipulation/navigation tasks and 0.31 on mobile manipulation tasks in simulation, exceeding the strong baseline $\pi_{0.5}$ by +0.20 and +0.11, respectively.
