Explainable AI for Robot Failures: Generating Explanations That Improve User Assistance in Fault Recovery

Devleena Das, Siddhartha Banerjee, Sonia Chernova

Abstract:

With the growing capabilities of intelligent systems, the integration of artificial intelligence (AI) and robots in everyday
life is increasing. However, when interacting in such complex human environments, the failure of intelligent systems,
such as robots, can be inevitable, requiring recovery assistance from users. In this work, we develop automated, natural language explanations for failures encountered during an
AI agents’ plan execution. These explanations are developed
with a focus of helping non-expert users understand different
point of failures to better provide recovery assistance. Specifically, we introduce a context-based information type for explanations that can both help non-expert users understand the
underlying cause of a system failure, and select proper failure
recoveries. Additionally, we extend an existing sequence-tosequence methodology to automatically generate our contextbased explanations. By doing so, we are able develop a model
that can generalize context-based explanations over both different failure types and failure scenarios.