STMA: A Spatio-Temporal Memory Agent for Long-Horizon Embodied Task
  Planning
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.
67b29f472ea5fd965beb91ed
Mingcong Lei
claimed_verified
2025-02-20T15:53:20.170Z
6628c6107751d297d7025a71
https://cdn-avatars.huggingface.co/v1/production/uploads/6628c6107751d297d7025a71/S1rm5VIwV2Uxfv8GetKMU.jpeg
Lei Mingcong
user
SP4595
67b29f472ea5fd965beb91ee
Yiming Zhao
67b29f472ea5fd965beb91ef
Ge Wang
67b29f472ea5fd965beb91f0
Zhixin Mai
67b29f472ea5fd965beb91f1
Shuguang Cui
67b29f472ea5fd965beb91f2
Yatong Han
67b29f472ea5fd965beb91f3
Jinke Ren
STMA: A Spatio-Temporal Memory Agent for Long-Horizon Embodied Task
  Planning
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.
The Spatio-Temporal Memory Agent (STMA) improves embodied intelligence by integrating spatio-temporal memory to enhance task planning and execution, resulting in superior performance in complex environments.
spatio-temporal memory spatio-temporal memory module dynamic knowledge graph planner-critic mechanism TextWorld multi-step planning exploration embodied intelligence