Integrating Intrinsic and Extrinsic Explainability: The Relevance of Understanding Neural Networks for Human-Robot Interaction

TL;DR

This paper discusses integrating intrinsic and extrinsic explainability in humanoid robots, emphasizing how understanding neural networks enhances human-robot interaction and collaboration.

Contribution

It introduces the concept of combining intrinsic robot explanations with extrinsic environmental explanations to improve robot behavior and human understanding in neurorobotics.

Findings

Intrinsic explanations by robots improve human trust.

Extrinsic explanations from environment aid robot decision-making.

Combined explanations enhance human-robot collaboration.

Abstract

Explainable artificial intelligence (XAI) can help foster trust in and acceptance of intelligent and autonomous systems. Moreover, understanding the motivation for an agent's behavior results in better and more successful collaborations between robots and humans. However, not only can humans benefit from a robot's explanation but the robot itself can also benefit from explanations given to him. Currently, most attention is paid to explaining deep neural networks and black-box models. However, a lot of these approaches are not applicable to humanoid robots. Therefore, in this position paper, current problems with adapting XAI methods to explainable neurorobotics are described. Furthermore, NICO, an open-source humanoid robot platform, is introduced and how the interaction of intrinsic explanations by the robot itself and extrinsic explanations provided by the environment enable efficient robotic behavior.