As autonomous robots are deployed in increasingly complex environments, platform degradation, environmental uncertainties, and deviations from validated operation conditions can make it difficult for human partners to understand robot capabilities and limitations. The ability for a robot to self-assess its competency in dynamic and uncertain environments will be a crucial next step in successful human-robot teaming. This work presents and evaluates an Event-Triggered Generalized Outcome Assessment (ET-GOA) algorithm for autonomous agents to dynamically assess task confidence during execution. The algorithm uses a fast online statistical test of the agent's observations and its model predictions to decide when competency assessment is needed. We provide experimental results using ET-GOA to generate competency reports during a simulated delivery task and suggest future research directions for self-assessing agents.

翻译：随着自主机器人在日益复杂的环境中部署，平台退化、环境不确定性以及偏离已验证操作条件等因素，可能使人类伙伴难以理解机器人的能力与局限性。机器人在动态和不确定环境中自评估其能力的能力，将成为实现成功人机团队协作的关键下一步。本文提出并评估了一种用于自主代理的事件触发广义结果评估（ET-GOA）算法，使代理能够在执行过程中动态评估任务置信度。该算法通过对代理的观测数据及其模型预测进行快速在线统计检验，以确定何时需要开展能力评估。我们通过模拟配送任务中的实验，展示了利用ET-GOA生成能力评估报告的结果，并提出了自评估代理的未来研究方向。