StratMamba：用于基于路径高效的激光雷达避障的策略性和反应性流划分
StratMamba: Strategic and Reactive Stream Partitioning for Path-Efficient LiDAR-Based Obstacle Avoidance
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中文总结 AI 辅助
研究针对复杂环境中机器人导航问题，提出StratMamba双流时间建模架构，结合快慢衰减内存架构处理激光雷达数据。经多场景评估及与其他基线对比，其在时间推理效率、导航速度和路径最优性方面表现出色，在现实中性能更稳健。
中文摘要 AI 辅助
本文提出了StratMamba，一种基于双流Mamba的时间建模架构，以更有效地捕捉复杂且障碍物多的环境中机器人导航所需的长期时间依赖性。StratMamba利用快速衰减和缓慢衰减内存架构的组合，快速衰减组件处理高频激光雷达数据以进行反应性避障，缓慢衰减组件维护长期目标信息用于策略规划。在IsaacLab和Gazebo中对不同避障场景进行了广泛评估，并在Unitree GO1四足机器人上验证了从模拟到现实的成功部署。与其他时间RL基线比较表明，StratMamba以更低的超时率实现了出色的时间推理效率，同时保持最快导航速度，还实现了最高路径最优性。现实世界评估显示，与普通Mamba和Transformer相比，StratMamba在扩展激光雷达范围内保持更稳健性能，证明双流划分在具有挑战性的传感条件下有效平衡了反应性安全与策略性导航。
英文摘要
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.
发表机构
- The Pennsylvania State University（宾夕法尼亚州立大学）
- AlphaZ, Inc.（阿尔法兹公司）
- Georgia Institute of Technology（佐治亚理工学院）
- Massachusetts Institute of Technology（麻省理工学院）
机构由 AI 辅助整理，请以论文原文为准。
