Explainable Robot Design Based on a Robot Theory of Mind: A Web-Based Platform to systematically evaluate Robot Designs

Kerstin Haring, Pilyoung Kim, Daniel Pittman

TL;DR: we investigate how the explanation of a robot's initially perceived capabilities is based on its surface-level clues and morphology using mind perception

Abstract: In this work, we investigate how the explanation of a robot's initially perceived capabilities is based on its surface-level clues and morphology. We explore how explainable robots tie into Robot Theory of Mind (RToM), a term we use to describe how people develop a mental model of a robot. We have developed a web-based platform to collect robot designs that are expected to correspond to mental states. We will train a set of Machine Learning (ML) models focused on feature extraction, validation of desired robot design attributes, and eventually use this as a tool to generate new robot designs targeting designs that provide an initial explanation about the robot's capabilities. We propose a series of neuroscientific studies to iteratively verify the outcomes from the data collection and the ML models training on data provided by the Build-A-Bot platform.