Towards explainability in robotics: A performance analysis of a cloud accountability system

Abstract

Understanding why a robot's behaviour was triggered is a growing concern to get human‐acceptable social robots. Every action, expected and unexpected, should be able to be explained and audited. The formal model proposed here deals with different information levels, from low‐level data, such as sensors' data logging; to high‐level data that provide an explanation of the robot's behaviour. This study examines the impact on the robot system of a custom log engine based on a custom ROS logging node and investigates pros and cons when used together with a NoSQL database locally and in a cloud environment. Results allow to characterize these alternatives and explore the best strategy for offering a fully log‐based accountability engine that maximizes the mapping between robot behaviour and robot logs.

Causal-Based Approaches to Explain and Learn from Self-Extension—A Review

“…We can set the departure point in the problem of the robot explaining its actions when they are the result of an internal planning process. Those explanations are usually oriented to provide a humanly understandable representation for debugging purposes or to determine liability, or eligibility in autonomous operations [15,16]. However, here we will also review the different problems of providing explanations that can be associated with real communication with the person [17].…”