STMA: A Spatio-Temporal Memory Agent for Long-Horizon Embodied Task Planning
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arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866929737706766336 |
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| author | Lei, Mingcong Zhao, Yiming Wang, Ge Mai, Zhixin Cui, Shuguang Han, Yatong Ren, Jinke |
| author_facet | Lei, Mingcong Zhao, Yiming Wang, Ge Mai, Zhixin Cui, Shuguang Han, Yatong Ren, Jinke |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_10177 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | STMA: A Spatio-Temporal Memory Agent for Long-Horizon Embodied Task Planning Lei, Mingcong Zhao, Yiming Wang, Ge Mai, Zhixin Cui, Shuguang Han, Yatong Ren, Jinke Artificial Intelligence 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. |
| title | STMA: A Spatio-Temporal Memory Agent for Long-Horizon Embodied Task Planning |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2502.10177 |