GLST: Global-Local Spatio-Temporal Memory Decomposition Framework
Overview
GLST framework improves online chained-goal navigation via memory decomposition, validated in simulation and real-world experiments with Spot robot, outperforming baselines in partially observable environments.
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Environment: simulation
“mporal representations for long-horizon reasoning. We further introduce goal-conditioned rewards to balance exploration and exploitation under evolving goals. Extensive experiments in simulation across diverse terrains demonstrate improved chained-goal navigation performance over baselines. Real-robot experiments using the Spot quadruped further validate our memory decomposition framework.”
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- arXiv Robotics — research abstractsGLST: Global-Local Spatio-Temporal Memory Decomposition Framework for Online Chained-Goal Navigation
This research proposes GLST, a memory-decomposition framework for online chained-goal navigation in partially observable environments. The method separates latent beliefs into global-local spatial and long-short temporal representations to support long-horizon reasoning. Goal-conditioned rewards are introduced to balance exploration and exploitation. The approach is validated through simulation and real-robot experiments using the Spot quadruped, demonstrating improved performance over existing baselines.
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