Toward brain-trained humanoid robots.

A growing corpus of experimental research demonstrates that neuroimaging data can be leveraged to improve the performance of artificial intelligence models, including reinforcement learning models in robotics. However, most existing studies have focused on vision, audition, language, or reward and error signals recorded while the human brain acts as an observer. In this paper, we argue that humanoid robots, although not equivalent to humans, could offer a unique opportunity to use the neural signals associated with touch, or other embodiment-dependent neural signals. As a result, humanoid robots could significantly expand the range of brain regions and functional networks that could be targeted for brain-trained artificial intelligence, and open new horizons in terms of alignment and reasoning. We discuss the improvements that neuroimaging data could potentially unlock in the context of sensorimotor tasks, spatial navigation, sensorimotor integration, embodied alignment, embodied reasoning, and social integration, as well as the broader challenges and opportunities related to brain-trained humanoid robots.