aiqbench analysis
This research addresses the challenge of online chained-goal navigation by introducing GLST, a framework that decomposes spatio-temporal memory to handle evolving objectives without explicit transition signals. Validated through both simulation and real-world experiments on the Spot quadruped, the method effectively balances exploration and exploitation in long-horizon, partially observable environments. The work represents a significant advancement in autonomous robotics by enabling agents to adapt dynamically as goals shift during execution.
GLST: Global-Local Spatio-Temporal Memory Decomposition Framework for Online Chained-Goal Navigation
arXiv:2610.17927v1 Announce Type: cross Abstract: Autonomous navigation in real-world environments often requires long-horizon operation under partial observability and evolving goals. Existing mapless navigation approaches predominantly assume fixed goals throughout an episode, limiting applicability to practical scenarios where goals change online during execution. We study online chained-goal navigation, where goals evolve through existing-goal redirection or new-goal emergence without explicit transition signals. Under this setting, we find navigation may require either continued exploration or strategic revisitation, creating conflicting memory requirements. We propose Global-Local Spatio-Temporal Belief Learning (GLST), a memory-decomposition framework that separates latent beliefs into global-local spatial and long-short temporal 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.
Abstract:Autonomous navigation in real-world environments often requires long-horizon operation under partial observability and evolving goals. Existing mapless navigation approaches predominantly assume fixed goals throughout an episode, limiting applicability to practical scenarios where goals change online during execution. We study online chained-goal navigation, where goals evolve through existing-goal redirection or new-goal emergence without explicit transition signals. Under this setting, we find navigation may require either continued exploration or strategic revisitation, creating conflicting memory requirements. We propose Global-Local Spatio-Temporal Belief Learning (GLST), a memory-decomposition framework that separates latent beliefs into global-local spatial and long-short temporal 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.
Robotics (cs.RO); Artificial Intelligence (cs.AI)
arXiv:2610.17927 [cs.RO]
(or arXiv:2610.17927v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2610.17927 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Full text under the publisher's open licence · 232 words
Related coverage
Triton-Based Optimization of Video Sparse Attention on ROCm
AMD · Jul 13, 2026
Learning Long Range Spatio-Temporal Representations over Continuous Time Dynamic Graphs with State Space Models
arXiv · Jun 6, 2026
Learning Long Range Spatio-Temporal Representations over Continuous Time Dynamic Graphs with State Space Models
arXiv · Jun 4, 2026
LiveVVT: High-Fidelity Video Virtual Try-On in Real Time
arXiv · Aug 28, 2026