Explainable AI for Robot Failures: Generating Explanations that Improve User Assistance in Fault Recovery
This work is significant for improving human-robot interaction by enabling non-expert users to better assist robots in recovering from failures, which is crucial for broader robot adoption in everyday environments.
This paper addresses the problem of explaining robot failures to non-expert users to improve fault recovery. The authors found that explanations detailing the context of a failure and the history of past actions were most effective for non-experts in identifying failures and solutions. Their autonomously generated explanations generalized to an unseen office environment and were as effective as hand-scripted explanations.
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.