When neuro-robots go wrong: A review

Abstract

Neuro-robots are a class of autonomous machines that, in their architecture, mimic aspects of the human brain and cognition. As such, they represent unique artifacts created by humans based on human understanding of healthy human brains. European Union's Convention on Roboethics 2025 states that the design of all robots (including neuro-robots) must include provisions for the complete traceability of the robots' actions, analogous to an aircraft's flight data recorder. At the same time, one can anticipate rising instances of neuro-robotic failure, as they operate on imperfect data in real environments, and the underlying AI behind such neuro-robots has yet to achieve explainability. This paper reviews the trajectory of the technology used in neuro-robots and accompanying failures. The failures demand an explanation. While drawing on existing explainable AI research, we argue explainability in AI limits the same in neuro-robots. In order to make robots more explainable, we suggest potential pathways for future research.

Keywords: explainability; explainable AI (X-AI); explainable neuro-robots; neuro-robotic failures; neuro-robotic models; neuro-robotic systems; responsible neuro-robots.

FIGURE 1

IHMC’s Atlas was one of the robots that fell during the DARPA robotics challenge finals. Source: DARPA (Guizzoevan and Ackerman, 2015).