Active inference is a mathematical framework that originated in computational neuroscience. Recently, it has been demonstrated as a promising approach for constructing goal-driven behavior in robotics. Specifically, the active inference controller (AIC) has been successful on several continuous control and state-estimation tasks. Despite its relative success, some established design choices lead to a number of practical limitations for robot control. These include having a biased estimate of the state, and only an implicit model of control actions. In this paper, we highlight these limitations and propose an extended version of the unbiased active inference controller (u-AIC). The u-AIC maintains all the compelling benefits of the AIC and removes its limitations. Simulation results on a 2-DOF arm and experiments on a real 7-DOF manipulator show the improved performance of the u-AIC with respect to the standard AIC. The code can be found at https://github.com/cpezzato/unbiased_aic.

翻译：主动推定是一个数学框架,起源于计算神经科学。最近,它被证明是构建机器人中目标驱动行为的一种很有希望的方法。具体地说,主动推定控制器(AIC)成功地完成了若干连续控制和国家估计任务。尽管它相对成功,但一些既定的设计选择导致对机器人控制的一些实际限制,其中包括对状态的偏差估计,而只是控制行动的隐含模式。在本文中,我们强调了这些限制,并提出了公正主动推断控制器(u-AIC)的扩大版本。U-AIC保持了AIC的所有令人信服的好处,并消除了它的局限性。对2-DOF臂的模拟结果和对7-DOF实际操纵器的实验显示,U-AIC在标准AIC方面的性能有所改善。该代码见https://github.com/cpezzato/unbiased_aic。