Robots performing navigation tasks in complex environments face significant challenges due to uncertainty in state estimation. Effectively managing this uncertainty is crucial, but the optimal approach varies depending on the specific details of the task: different tasks require varying levels of precision in different regions of the environment. For instance, a robot navigating a crowded space might need precise localization near obstacles but can operate effectively with less precise state estimates in open areas. This varying need for certainty in different parts of the environment, depending on the task, calls for policies that can adapt their uncertainty management strategies based on task-specific requirements. In this paper, we present a framework for integrating task-specific uncertainty requirements directly into navigation policies. We introduce Task-Specific Uncertainty Map (TSUM), which represents acceptable levels of state estimation uncertainty across different regions of the operating environment for a given task. Using TSUM, we propose Generalized Uncertainty Integration for Decision-Making and Execution (GUIDE), a policy conditioning framework that incorporates these uncertainty requirements into the robot's decision-making process. We find that conditioning policies on TSUMs provides an effective way to express task-specific uncertainty requirements and enables the robot to reason about the context-dependent value of certainty. We show how integrating GUIDE into reinforcement learning frameworks allows the agent to learn navigation policies without the need for explicit reward engineering to balance task completion and uncertainty management. We evaluate GUIDE on a variety of real-world navigation tasks and find that it demonstrates significant improvements in task completion rates compared to baselines. Evaluation videos can be found at https://guided-agents.github.io.

翻译：机器人在复杂环境中执行导航任务时，由于状态估计的不确定性而面临重大挑战。有效管理这种不确定性至关重要，但最优方法因任务具体细节而异：不同任务对环境不同区域所需的精度要求各不相同。例如，在拥挤空间中导航的机器人可能需要在障碍物附近进行精确定位，但在开阔区域使用精度较低的状态估计仍能有效运行。这种根据任务需求在不同环境区域对确定性程度的不同要求，需要能够基于任务特定需求调整其不确定性管理策略的导航策略。本文提出一种将任务特定不确定性要求直接集成到导航策略中的框架。我们引入任务特定不确定性地图，该地图针对给定任务表示操作环境不同区域可接受的状态估计不确定性水平。基于TSUM，我们提出用于决策与执行的广义不确定性集成框架，这是一种将此类不确定性要求纳入机器人决策过程的策略调节框架。我们发现，基于TSUM调节策略为表达任务特定不确定性要求提供了有效途径，并使机器人能够推理确定性在不同情境下的价值。我们展示了将GUIDE集成到强化学习框架中，可使智能体无需通过显式奖励工程来平衡任务完成与不确定性管理，即可学习导航策略。我们在多种现实世界导航任务上评估GUIDE，发现其任务完成率相比基线方法有显著提升。评估视频可在https://guided-agents.github.io查看。