arXiv：cs.AI· Yousef Liang, Ryan Ardalan, Yarden Goraly, Chenhao Zhu, Yue Yin, Jonas Frey, Claire Tomlin, Marco Pavone, Shankar Sastry·· 22 小时前
GLST：面向在线链式目标导航的全局-局部时空记忆分解框架
GLST: Global-Local Spatio-Temporal Memory Decomposition Framework for Online Chained-Goal Navigation
AI 导读
针对目标可在执行中在线变化、且无显式切换信号的在线链式目标导航，研究者提出记忆分解框架 GLST，将隐式信念拆分为全局-局部空间与长-短时序表示以支持长程推理。该框架引入目标条件奖励以平衡探索与利用，仿真多地形实验中链式目标导航表现优于基线，并用 Spot 四足机器人完成真实机器人验证。论文已被 CoRL 2026 接收。
正文
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.
| Comments: | Accepted at the Conference on Robot Learning (CoRL) 2026 |
| Subjects: | Robotics (cs.RO); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.17927 [cs.RO] |
| (or arXiv:2610.17927v1 [cs.RO] for this version) | |
| https://doi.org/10.48550/arXiv.2610.17927 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Yousef Liang [view email]
[v1]
Tue, 13 Oct 2026 08:15:12 UTC (5,140 KB)
来源：arXiv：cs.AI · arxiv.org