用时空记忆提升智能体长时任务规划能力
STMA: A Spatio-Temporal Memory Agent for Long-Horizon Embodied Task Planning
- 引入时空记忆模块实时记录环境变化
- 在TextWorld上任务成功率提升31.25%
- 适合需要长期规划的机器人与AI代理研究
实现智能体在动态环境中执行长时任务并保持稳健决策与适应性，是具身智能的关键目标。为此，我们提出时空记忆代理（STMA），通过融合时空记忆来增强任务规划与执行能力。STMA包含三个核心组件：(1) 时空记忆模块，实时捕捉历史与环境变化；(2) 动态知识图谱，支持自适应空间推理；(3) 计划-批评机制，迭代优化任务策略。我们在TextWorld环境的32个任务上评估STMA，涵盖多步规划与探索，复杂度各异。实验表明，相较于最先进模型，STMA成功率达到31.25%的提升，平均得分提高24.7%。结果验证了时空记忆在增强具身智能体记忆能力方面的有效性。
原文摘要 · Abstract (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.
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