BrainMem: 身体性エージェントのタスク計画のための脳に着想を得た進化する記憶
BrainMem: Brain-Inspired Evolving Memory for Embodied Agent Task Planning
人間の認知に着想を得た、訓練不要の階層的記憶システムを提案し、エージェントの過去の経験を知識グラフと記号的ガイドラインに変換して、LLMベースのプランナーが長期的なタスクをより成功させることを実証した。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
4. どうやって有効だと検証した？
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6. 次に読むべき論文は？
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著者: Xiaoyu Ma, Lianyu Hu, Wenbing Tang, Zixuan Hu, Zeqin Liao, Zhizhen Wu, Yang Liu
分類: cs.RO, cs.AI, cs.CV, cs.MA
原文アブストラクト
Embodied task planning requires agents to execute long-horizon, goal-directed actions in complex 3D environments, where success depends on both immediate perception and accumulated experience across tasks. However, most existing LLM-based planners are stateless and reactive, operating without persistent memory and therefore repeating errors and struggling with spatial or temporal dependencies. We propose BrainMem(Brain-Inspired Evolving Memory), a training-free hierarchical memory system that equips embodied agents with working, episodic, and semantic memory inspired by human cognition. BrainMem continuously transforms interaction histories into structured knowledge graphs and distilled symbolic guidelines, enabling planners to retrieve, reason over, and adapt behaviors from past experience without any model fine-tuning or additional training. This plug-and-play design integrates seamlessly with arbitrary multi-modal LLMs and greatly reduces reliance on task-specific prompt engineering. Extensive experiments on four representative benchmarks, including EB-ALFRED, EB-Navigation, EB-Manipulation, and EB-Habitat, demonstrate that BrainMem significantly enhances task success rates across diverse models and difficulty subsets, with the largest gains observed on long-horizon and spatially complex tasks. These results highlight evolving memory as a promising and scalable mechanism for generalizable embodied intelligence.
