  Mobile agents that can leverage help from humans can potentially accomplish
more complex tasks than they could entirely on their own. We develop "Help,
Anna!" (HANNA), an interactive photo-realistic simulator in which an agent
fulfills object-finding tasks by requesting and interpreting natural
language-and-vision assistance. An agent solving tasks in a HANNA environment
can leverage simulated human assistants, called ANNA (Automatic Natural
Navigation Assistants), which, upon request, provide natural language and
visual instructions to direct the agent towards the goals. To address the HANNA
problem, we develop a memory-augmented neural agent that hierarchically models
multiple levels of decision-making, and an imitation learning algorithm that
teaches the agent to avoid repeating past mistakes while simultaneously
predicting its own chances of making future progress. Empirically, our approach
is able to ask for help more effectively than competitive baselines and, thus,
attains higher task success rate on both previously seen and previously unseen
environments. We publicly release code and data at
https://github.com/khanhptnk/hanna . A video demo is available at
https://youtu.be/18P94aaaLKg .
