STMA: A Spatio-Temporal Memory Agent for Long-Horizon Embodied Task Planning
This addresses the challenge of robust decision-making and adaptability for embodied agents in dynamic settings, representing a strong specific gain rather than a foundational advancement.
The paper tackled the problem of enabling embodied agents to perform long-horizon tasks in dynamic environments by proposing the Spatio-Temporal Memory Agent (STMA), which achieved a 31.25% improvement in success rate and a 24.7% increase in average score compared to the state-of-the-art model in the TextWorld environment.
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.