  Robots operating alongside humans often encounter unfamiliar environments
that make autonomous task completion challenging. Though improving models and
increasing dataset size can enhance a robot's performance in unseen
environments, data collection and model refinement may be impractical in every
environment. Approaches that utilize human demonstrations through manual
operation can aid in refinement and generalization, but often require
significant data collection efforts to generate enough demonstration data to
achieve satisfactory task performance. Interactive approaches allow for humans
to provide correction to robot action in real time, but intervention policies
are often based on explicit factors related to state and task understanding
that may be difficult to generalize. Addressing these challenges, we train a
lightweight interaction policy that allows robots to decide when to proceed
autonomously or request expert assistance at estimated times of uncertainty. An
implicit estimate of uncertainty is learned via evaluating the feature
extraction capabilities of the robot's visual navigation policy. By
incorporating part-time human interaction, robots recover quickly from their
mistakes, significantly improving the odds of task completion. Incorporating
part-time interaction yields an increase in success of 0.38 with only a 0.3
expert interaction rate within the Habitat simulation environment using a
simulated human expert. We further show success transferring this approach to a
new domain with a real human expert, improving success from less than 0.1 with
an autonomous agent to 0.92 with a 0.23 human interaction rate. This approach
provides a practical means for robots to interact and learn from humans in
real-world settings.
