Interpretability of a Service Robot: Enabling User Questions and Checkable Answers

12 pages•Published: September 17, 2018

Abstract

Service robots are becoming more and more capable but at the same time they are opaque to their users. Once a robot starts executing a task it is hard to tell what it is doing or why. To make robots more transparent to their users we propose to expand the capabilities of robots to not only execute tasks but also answer questions about their experience.
During execution, our CoBot robots record log files. We propose to use these files as a recording of the robot experience. Log files record the experience of the robot in term of its internals. To process information from the logs we define Log Primitives Operations (LPOs) that the robot can autonomously perform. Each LPO is defined in terms of an operation and a set of filters. We frame the problem of understanding questions about robot past experiences, as grounding input sentences to LPOs. To do so, we introduce a probabilistic model to ground sentences to these primitives. We evaluate our approach on a corpus of 133 sentences showing that our method is able to learn the meaning of users’ questions.
Finally we introduce the concept of checkable answers to have the robot provide answers that better explain the computation performed to achieve the result reported.

Keyphrases: explainable ai, human robot interaction, mobile service robots

In: Daniel Lee, Alexander Steen and Toby Walsh (editors). GCAI-2018. 4th Global Conference on Artificial Intelligence, vol 55, pages 176-187.

Links:	https://easychair.org/publications/paper/q8bw
	https://doi.org/10.29007/tt18