让机器人根据任务需求动态调整导航中的不确定性管理
Enhancing Robot Navigation Policies with Task-Specific Uncertainty Managements
- 用任务特异性不确定性地图动态分配容错范围
- 实测在复杂环境中导航成功率提升显著
- 适合需要高适应性的自主机器人场景
在复杂环境中，机器人需应对传感器噪声、环境变化和信息不全带来的不确定性，且不同任务对精度要求各异。例如，靠近障碍物时定位精度至关重要，而在开阔区域则可容忍更高误差。本文提出GUIDE（Generalized Uncertainty Integration for Decision-Making and Execution）框架，通过任务特异性不确定性地图（TSUMs）将任务需求融入导航策略，为不同位置设定可接受的不确定性阈值，使机器人能根据上下文自适应调整。结合强化学习，GUIDE能在无需大量奖励工程的情况下，学习兼顾任务完成与不确定性控制的策略。真实世界测试表明，其性能显著优于缺乏任务特异性不确定性感知的方法。
原文摘要 · Abstract (English)
Robots navigating complex environments must manage uncertainty from sensor noise, environmental changes, and incomplete information, with different tasks requiring varying levels of precision in different areas. For example, precise localization may be crucial near obstacles but less critical in open spaces. We present GUIDE (Generalized Uncertainty Integration for Decision-Making and Execution), a framework that integrates these task-specific requirements into navigation policies via Task-Specific Uncertainty Maps (TSUMs). By assigning acceptable uncertainty levels to different locations, TSUMs enable robots to adapt uncertainty management based on context. When combined with reinforcement learning, GUIDE learns policies that balance task completion and uncertainty management without extensive reward engineering. Real-world tests show significant performance gains over methods lacking task-specific uncertainty awareness.
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