发表机构
Georgia Institute of Technology（佐治亚理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对机器人主动辅助缺乏统一表述和离线评估失真的问题，提出三层次框架及闭环评估方法，并开发GAP算法，通过被动观察预测用户目标，在闭环评估中显著优于现有方法。
AI 中文摘要
在狭窄角色和重复性任务之外提供有效的机器人辅助，要求机器人具备主动性——即决定需要做什么，而不是等待被指示。尽管主动性日益受到探索，但它缺乏统一的表述，且该领域的工作通常离线评估，针对静态的人类模型，这些模型无法捕捉机器人行动对环境及用户自身行为的影响。我们提出了一种统一的主动机器人辅助形式化体系，将其组织为三个层级，并提供了一个框架来处理最高级别的无提示主动辅助。随后，我们展示了离线评估在此情境下会夸大性能，并贡献了一种闭环评估方法，其中人类模型会适应机器人。最后，我们提出了一种方法GAP，它实例化了我们的框架，通过被动观察学习来预测用户目标并采取行动。在闭环评估下，先前最先进的方法性能崩溃，在某些情况下增加的工作量超过其节省的，而GAP保持稳健并显著优于它们。
英文摘要
Effective robot assistance beyond narrow roles and repetitive tasks requires robots to be proactive - to decide what needs to be done rather than waiting to be told. While proactivity is increasingly explored, it lacks a unified formulation, and work in the domain is typically evaluated offline against static human models that cannot capture the effect of a robot's actions on the environment and the user's own behavior. We introduce a unified formalism for proactive robot assistance, organize it into three levels, and provide a framework to address the highest level of unprompted proactive assistance. We then show that offline evaluation overstates performance in this setting, and contribute a closed-loop evaluation with a human model that adapts to the robot. Finally, we present a method, GAP, that instantiates our framework, learning from passive observation to anticipate user goals and act. Under closed-loop evaluation, prior state-of-the-art methods collapse, in some cases adding more work than they save, while GAP remains robust and substantially outperforms them.
