  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.
