论文标题
对经典控制的无偏主动性推断
Unbiased Active Inference for Classical Control
论文作者
论文摘要
主动推断是源自计算神经科学的数学框架。最近，它被证明是在机器人技术中构建目标驱动行为的一种有希望的方法。具体而言，主动推理控制器（AIC）在多个连续控制和状态估计任务上都成功了。尽管取得了相对成功，但一些建立的设计选择导致了机器人控制的许多实际限制。这些包括对国家有偏见的估计，也只有一个隐式控制动作模型。在本文中，我们强调了这些局限性，并提出了无偏见的活动推理控制器（U-AIC）的扩展版本。 U-AIC维持了AIC的所有引人注目的好处，并消除了其局限性。模拟在2多臂臂上的结果和实验在实际的7-DOF操纵器上的实验表明，相对于标准AIC，U-AIC的性能提高了。该代码可以在https://github.com/cpezzato/unbiased_aic上找到。
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.
