GUIDEd Agents: 通过在定位受限环境中任务特定的不确定性抽象增强导航策略
GUIDEd Agents: Enhancing Navigation Policies through Task-Specific Uncertainty Abstraction in Localization-Limited Environments
AI总结：
GUIDE通过任务特定不确定性抽象提升机器人在定位受限环境中的导航策略，实现任务完成与不确定性管理的平衡。
摘要：
自主车辆在复杂环境中执行导航任务时，由于状态估计的不确定性面临重大挑战。在许多场景中，例如隐蔽行动或资源受限的设置，获取高精度定位会带来显著成本，迫使机器人主要依赖于不精确的状态估计。我们的关键观察是，不同任务在不同区域需要不同的精度水平：一个在拥挤空间中导航的机器人可能需要在障碍物附近有精确的定位，但可以在其他地方使用较低的精度。在本文中，我们提出了一种将任务特定的不确定性要求直接整合到导航策略中的规划方法。我们引入了任务特定不确定性地图（TSUMs），它在不同区域抽象出可接受的状态估计不确定性水平。TSUMs通过一个领域适应的编码器在共享表示空间中对任务要求和环境特征进行对齐。使用TSUMs，我们提出了通用不确定性整合决策与执行（GUIDE），一种将这些不确定性要求整合到机器人决策中的策略条件框架。我们发现TSUMs为抽象任务特定的不确定性要求提供了一种有效的方法，将策略条件于TSUMs使机器人能够推理关于情境依赖的确定性的价值并相应地适应其行为。我们展示了将GUIDE整合到强化学习框架中如何使代理学习到能够平衡任务完成和不确定性管理的导航策略，而无需显式的奖励工程。我们评估了GUIDE在各种现实世界机器人导航任务上的表现，并发现其在任务完成率方面相比不显式考虑任务特定不确定性的基线方法有显著改进。
英文摘要：
Autonomous vehicles performing navigation tasks in complex environments face significant challenges due to uncertainty in state estimation. In many scenarios, such as stealth operations or resource-constrained settings, accessing high-precision localization comes at a significant cost, forcing robots to rely primarily on less precise state estimates. Our key observation is that different tasks require varying levels of precision in different regions: a robot navigating a crowded space might need precise localization near obstacles but can operate effectively with less precision elsewhere. In this paper, we present a planning method for integrating task-specific uncertainty requirements directly into navigation policies. We introduce Task-Specific Uncertainty Maps (TSUMs), which abstract the acceptable levels of state estimation uncertainty across different regions. TSUMs align task requirements and environmental features using a shared representation space, generated via a domain-adapted encoder. Using TSUMs, we propose Generalized Uncertainty Integration for Decision-Making and Execution (GUIDE), a policy conditioning framework that incorporates these uncertainty requirements into robot decision-making. We find that TSUMs provide an effective way to abstract task-specific uncertainty requirements, and conditioning policies on TSUMs enables the robot to reason about the context-dependent value of certainty and adapt its behavior accordingly. We show how integrating GUIDE into reinforcement learning frameworks allows the agent to learn navigation policies that effectively balance task completion and uncertainty management without explicit reward engineering. We evaluate GUIDE on various real-world robotic navigation tasks and find that it demonstrates significant improvement in task completion rates compared to baseline methods that do not explicitly consider task-specific uncertainty.
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