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

• URL: http://arxiv.org/abs/2101.01625v1
• Date: Tue, 5 Jan 2021 16:16:39 GMT
• Title: Explainable AI for Robot Failures: Generating Explanations that Improve User Assistance in Fault Recovery
• Authors: Devleena Das, Siddhartha Banerjee, Sonia Chernova
• Abstract summary: We introduce a new type of explanation, that explains the cause of an unexpected failure during an agent's plan execution to non-experts. We investigate how such explanations can be autonomously generated, extending an existing encoder-decoder model.
• Score: 19.56670862587773
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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