More adaptive controllers for robot manipulators are needed, which can deal
with large model uncertainties. This paper presents a novel active inference
controller (AIC) as an adaptive control scheme for industrial robots. This
scheme is easily scalable to high degrees-of-freedom, and it maintains high
performance even in the presence of large unmodeled dynamics. The proposed
method is based on active inference, a promising neuroscientific theory of the
brain, which describes a biologically plausible algorithm for perception and
action. In this work, we formulate active inference from a control perspective,
deriving a model-free control law which is less sensitive to unmodeled
dynamics. The performance and the adaptive properties of the algorithm are
compared to a state-of-the-art model reference adaptive controller (MRAC) in an
experimental setup with a real 7-DOF robot arm. The results showed that the AIC
outperformed the MRAC in terms of adaptability, providing a more general
control law. This confirmed the relevance of active inference for robot
control.

本文提出了一种新型的主动推理控制器 (AIC)，它是一种适用于工业机器人的自适应控制方案，能够应对大型模型不确定性，同时还能在大型未建模动态的情况下保持高性能。该方法基于主动推理，从控制角度推导出的模型无关的控制定律，能够比目前最先进的模型参考自适应控制器 (MRAC) 更好地适应性，提供了一种更为通用的控制定律，这证实了主动推理对机器人控制的相关性。