STMA: A Spatio-Temporal Memory Agent for Long-Horizon Embodied Task
  Planning
Authors: name: Mingcong Lei, name: Yiming Zhao, name: Ge Wang, name: Zhixin Mai, name: Shuguang Cui, name: Yatong Han, name: Jinke Ren

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.