  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.
