  Recent progress in end-to-end Imitation Learning approaches has shown
promising results and generalization capabilities on mobile manipulation tasks.
Such models are seeing increasing deployment in real-world settings, where
scaling up requires robots to be able to operate with high autonomy, i.e.
requiring as little human supervision as possible. In order to avoid the need
for one-on-one human supervision, robots need to be able to detect and prevent
policy failures ahead of time, and ask for help, allowing a remote operator to
supervise multiple robots and help when needed. However, the black-box nature
of end-to-end Imitation Learning models such as Behavioral Cloning, as well as
the lack of an explicit state-value representation, make it difficult to
predict failures. To this end, we introduce Behavioral Cloning Value
Approximation (BCVA), an approach to learning a state value function based on
and trained jointly with a Behavioral Cloning policy that can be used to
predict failures. We demonstrate the effectiveness of BCVA by applying it to
the challenging mobile manipulation task of latched-door opening, showing that
we can identify failure scenarios with with 86% precision and 81% recall,
evaluated on over 2000 real world runs, improving upon the baseline of simple
failure classification by 10 percentage-points.
