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

SP4595, Yiming Zhao, Ge Wang, Zhixin Mai, Shuguang Cui, Yatong Han, Jinke Ren

STMA: A Spatio-Temporal Memory Agent for Long-Horizon Embodied Task Planning: 6 upvotes on Hugging Face Daily Papers, #17 of 23 papers on 2025-02-17. Day-by-day upvote history.

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 page on Hugging Face · arXiv

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