When deployed, AI agents will encounter problems that are beyond their autonomous problem-solving capabilities. Leveraging human assistance can help agents overcome their inherent limitations and robustly cope with unfamiliar situations. We present a general interactive framework that enables an agent to request and interpret rich, contextually useful information from an assistant that has knowledge about the task and the environment. We demonstrate the practicality of our framework on a simulated human-assisted navigation problem. Aided with an assistance-requesting policy learned by our method, a navigation agent achieves up to a 7x improvement in success rate on tasks that take place in previously unseen environments, compared to fully autonomous behavior. We show that the agent can take advantage of different types of information depending on the context, and analyze the benefits and challenges of learning the assistance-requesting policy when the assistant can recursively decompose tasks into subtasks.

翻译：当部署时,AI代理商将遇到超出其自主解决问题能力范围的问题。利用人类援助可以帮助代理商克服其固有的局限性并强有力地应对不熟悉的情况。我们提出了一个一般性的互动框架,使代理商能够请求和解释有关任务和环境的助理提供的丰富、符合背景的有用信息。我们展示了我们模拟人力辅助导航问题框架的实用性。借助我们方法所学的援助请求政策,导航代理商在以往的不为人知的环境中执行的任务的成功率比完全自主的行为提高了7x倍。我们表明该代理商能够根据具体情况利用不同类型的信息,并分析在助理能够将任务反复转换成子任务时学习援助请求政策的好处和挑战。