  Embodied AI agents continue to become more capable every year with the advent
of new models, environments, and benchmarks, but are still far away from being
performant and reliable enough to be deployed in real, user-facing,
applications. In this paper, we ask: can we bridge this gap by enabling agents
to ask for assistance from an expert such as a human being? To this end, we
propose the Ask4Help policy that augments agents with the ability to request,
and then use expert assistance. Ask4Help policies can be efficiently trained
without modifying the original agent's parameters and learn a desirable
trade-off between task performance and the amount of requested help, thereby
reducing the cost of querying the expert. We evaluate Ask4Help on two different
tasks -- object goal navigation and room rearrangement and see substantial
improvements in performance using minimal help. On object navigation, an agent
that achieves a $52\%$ success rate is raised to $86\%$ with $13\%$ help and
for rearrangement, the state-of-the-art model with a $7\%$ success rate is
dramatically improved to $90.4\%$ using $39\%$ help. Human trials with Ask4Help
demonstrate the efficacy of our approach in practical scenarios. We release the
code for Ask4Help here: https://github.com/allenai/ask4help.
