Explainable AI for Robot Failures: Generating Explanations that Improve
  User Assistance in Fault Recovery
  With the growing capabilities of intelligent systems, the integration of
robots in our everyday life is increasing. However, when interacting in such
complex human environments, the occasional failure of robotic systems is
inevitable. The field of explainable AI has sought to make complex-decision
making systems more interpretable but most existing techniques target domain
experts. On the contrary, in many failure cases, robots will require recovery
assistance from non-expert users. In this work, we introduce a new type of
explanation, that explains the cause of an unexpected failure during an agent's
plan execution to non-experts. In order for error explanations to be
meaningful, we investigate what types of information within a set of
hand-scripted explanations are most helpful to non-experts for failure and
solution identification. Additionally, we investigate how such explanations can
be autonomously generated, extending an existing encoder-decoder model, and
generalized across environments. We investigate such questions in the context
of a robot performing a pick-and-place manipulation task in the home
environment. Our results show that explanations capturing the context of a
failure and history of past actions, are the most effective for failure and
solution identification among non-experts. Furthermore, through a second user
evaluation, we verify that our model-generated explanations can generalize to
an unseen office environment, and are just as effective as the hand-scripted
explanations.
