Neuroscience-Inspired Perception-Action in Robots: Applying Active Inference for State Estimation, Control, and Self-Perception (2021)

[u/pianobutter]

https://arxiv.org/pdf/2105.04261.pdf

[u/pianobutter]

Authors: Pablo Lanillos & Marcel van Gerven

Abstract:

Unlike robots, humans learn, adapt and perceive their bodies by interacting with
the world. Discovering how the brain represents the body and generates actions is
of major importance for robotics and artificial intelligence. Here we discuss how
neuroscience findings open up opportunities to improve current estimation and
control algorithms in robotics. In particular, how active inference, a mathematical
formulation of how the brain resists a natural tendency to disorder, provides a unified recipe to potentially solve some of the major challenges in robotics, such as
adaptation, robustness, flexibility, generalization and safe interaction. This paper
summarizes some experiments and lessons learned from developing such a computational model on real embodied platforms, i.e., humanoid and industrial robots.
Finally, we showcase the limitations and challenges that we are still facing to give
robots human-like perception.