title: STMA: A Spatio-Temporal Memory Agent for Long-Horizon Embodied Task
    Planning
summary: 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.
paper_authors._id: 67b29f472ea5fd965beb91ed
paper_authors.name: Mingcong Lei
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-20T15:53:20.170Z
paper_authors._id: 6628c6107751d297d7025a71
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/6628c6107751d297d7025a71/S1rm5VIwV2Uxfv8GetKMU.jpeg
paper_authors.fullname: Lei Mingcong
paper_authors.type: user
paper_authors.user: SP4595
paper_authors._id: 67b29f472ea5fd965beb91ee
paper_authors.name: Yiming Zhao
paper_authors._id: 67b29f472ea5fd965beb91ef
paper_authors.name: Ge Wang
paper_authors._id: 67b29f472ea5fd965beb91f0
paper_authors.name: Zhixin Mai
paper_authors._id: 67b29f472ea5fd965beb91f1
paper_authors.name: Shuguang Cui
paper_authors._id: 67b29f472ea5fd965beb91f2
paper_authors.name: Yatong Han
paper_authors._id: 67b29f472ea5fd965beb91f3
paper_authors.name: Jinke Ren
paper_title: STMA: A Spatio-Temporal Memory Agent for Long-Horizon Embodied Task
    Planning
paper_summary: 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.
paper_ai_keywords: Spatio-Temporal Memory Agents (STMA) spatio-temporal memory module dynamic knowledge graph adaptive spatial reasoning planner-critic mechanism multi-step planning exploration