Improving Robot Controller Transparency Through Autonomous Policy Explanation

TitleImproving Robot Controller Transparency Through Autonomous Policy Explanation
Publication TypeConference Proceedings
Year of Conference2017
AuthorsHayes, B., and J. A. Shah
Conference Name12th ACM/IEEE International Conference on Human-Robot Interaction (HRI 2017)
Date Published03/2017

Shared expectations and mutual understanding are critical facets of teamwork. Achieving these in human-robot collaborative contexts can be especially challenging, as humans and robots are unlikely to share a common language to convey intentions, plans, or justifications. Even in cases where human co-workers can inspect a robot’s control code, and particularly when statistical methods are used to encode control policies, there is no guarantee that meaningful insights into a robot’s behavior can be derived or that a human will be able to efficiently isolate the behaviors relevant to the interaction. We present a series of algorithms and an accompanying system that enables robots to autonomously synthesize policy descriptions and respond to both general and targeted queries by human collaborators. We demonstrate applicability to a variety of robot controller types including those that utilize conditional logic, tabular reinforcement learning, and deep reinforcement learning, synthesizing informative policy descriptions for collaborators and facilitating fault diagnosis by non-experts.