论文/arXiv 2607.12370·3 个月前

StratMamba：面向路径高效 LiDAR 避障的战略与反应式流划分

StratMamba用双流Mamba时序建模分离反应避障与战略规划，并在IsaacLab、Gazebo和GO1真机上验证。

AI 整理

• 本体 SDK 与控制
• 感知与导航
• 仿真与训练

原文链接：StratMamba: Strategic and Reactive Stream Partitioning for Path-Efficient LiDAR-Based Obstacle Avoidance（arXiv 2607.12370 · 发布日期：2026-07-14）

StratMamba: Strategic and Reactive Stream Partitioning for Path-Efficient LiDAR-Based Obstacle Avoidance

StratMamba用双流Mamba时序建模分离反应避障与战略规划，并在IsaacLab、Gazebo和GO1真机上验证。

核心要点

• 复杂且障碍密集环境中的机器人导航，需要高效捕获长时程时序依赖。
• 方法结合快衰减与慢衰减记忆，分别处理高频LiDAR与长时程目标信息。
• 在IsaacLab和Gazebo中对比LSTM、Transformer等，降低超时率并提升路径效率。
• GO1真机静态/动态障碍实验中，扩展LiDAR范围下表现更稳健。

验证范围与局限：验证范围包括IsaacLab和Gazebo仿真避障场景，以及Unitree GO1在静态/动态障碍中的真机部署。

英文摘要 (Abstract)

This paper proposes StratMamba, a dual-stream Mamba-based temporal modeling architecture, to more efficiently capture long-horizon temporal dependencies required for robot navigation in complex and obstacle-rich environments. StratMamba leverages a combination of fast-decay and slow-decay memory architectures, where the fast-decay component processes high-frequency LiDAR data for reactive obstacle avoidance, while the slow-decay component maintains longer-horizon goal information for strategic planning. We perform extensive evaluations of different obstacle avoidance scenarios in IsaacLab and Gazebo, while also validating successful sim-to-real deployment on a Unitree GO1 quadruped robot navigating in the presence of static/dynamic obstacles. Comparisons with other temporal RL baselines, such as LSTM, Transformer, and Vanilla-Mamba, show that our StratMamba achieves exceptional temporal reasoning efficiency with a lower timeout rate, while maintaining the fastest navigation speed (576 median steps, 5.0% better than Vanilla-Mamba). It also achieves the highest path optimality (0.915 path efficiency) across all baselines. Real-world evaluation reveals that StratMamba maintains more robust performance across extended LiDAR ranges compared to vanilla Mamba and the Transformer, demonstrating that dual-stream partitioning effectively balances reactive safety with strategic navigation under challenging sensing conditions.

作者：Hung-Chieh Wu, Xiaopan Zhang, Kasra Sinaei, Ryan Abnavi, Kasun Weerakoon, Christopher Bradley, Seyed Fakoorian, Jiachen Li, Donald Ebeigbe

来源

本页事实对应的公开页面，以原文为准。

1. arXiv 2607.12370：StratMamba: Strategic and Reactive Stream Partitioning for Path-Efficient LiDAR-Based Obstacle Avoidance访问 2026-10-21