STMA：面向长程具身任务规划的时空记忆智能体

STMA: A Spatio-Temporal Memory Agent for Long-Horizon Embodied Task Planning

February 14, 2025

作者: Mingcong Lei, Yiming Zhao, Ge Wang, Zhixin Mai, Shuguang Cui, Yatong Han, Jinke Ren

cs.AI

摘要

具身智能的一个核心目标是使智能体能够在动态环境中执行长期任务，同时保持稳健的决策能力和适应性。为实现这一目标，我们提出了时空记忆智能体（STMA），这是一个旨在通过整合时空记忆来增强任务规划与执行的全新框架。STMA建立在三个关键组件之上：(1) 一个实时捕捉历史与环境变化的时空记忆模块，(2) 一个促进自适应空间推理的动态知识图谱，以及(3) 一个迭代优化任务策略的规划-评估机制。我们在TextWorld环境中对STMA进行了评估，涉及32项任务，这些任务需要在不同复杂度下进行多步规划与探索。实验结果表明，与最先进的模型相比，STMA在任务成功率上提升了31.25%，平均得分提高了24.7%。这些结果凸显了时空记忆在提升具身智能体记忆能力方面的有效性。

English

A key objective of embodied intelligence is enabling agents to perform long-horizon tasks in dynamic environments while maintaining robust decision-making and adaptability. To achieve this goal, we propose the Spatio-Temporal Memory Agent (STMA), a novel framework designed to enhance task planning and execution by integrating spatio-temporal memory. STMA is built upon three critical components: (1) a spatio-temporal memory module that captures historical and environmental changes in real time, (2) a dynamic knowledge graph that facilitates adaptive spatial reasoning, and (3) a planner-critic mechanism that iteratively refines task strategies. We evaluate STMA in the TextWorld environment on 32 tasks, involving multi-step planning and exploration under varying levels of complexity. Experimental results demonstrate that STMA achieves a 31.25% improvement in success rate and a 24.7% increase in average score compared to the state-of-the-art model. The results highlight the effectiveness of spatio-temporal memory in advancing the memory capabilities of embodied agents.