论文标题
积极推断综合国家估计，控制和学习
Active Inference for Integrated State-Estimation, Control, and Learning
论文作者
论文摘要
这项工作提出了一种用于机器人操纵器的控制，国家估计和学习模型（超级）参数的方法。它基于主动推理框架，在计算神经科学作为大脑理论中突出，在这种框架中，行为是由于最大程度地减少变分的自由能。与最先进的方法相比，机器人操纵器表现出适应性和健壮的行为。此外，我们还显示了与PID控制等经典方法的确切关系。最后，我们表明，通过学习时间参数和模型差异，我们的方法可以处理未建模的动力学，潮湿的振荡，并且对干扰和初始参数较差具有强大的态度。该方法在“ Franka Emika Panda” 7 DOF操纵器上得到了验证。
This work presents an approach for control, state-estimation and learning model (hyper)parameters for robotic manipulators. It is based on the active inference framework, prominent in computational neuroscience as a theory of the brain, where behaviour arises from minimizing variational free-energy. The robotic manipulator shows adaptive and robust behaviour compared to state-of-the-art methods. Additionally, we show the exact relationship to classic methods such as PID control. Finally, we show that by learning a temporal parameter and model variances, our approach can deal with unmodelled dynamics, damps oscillations, and is robust against disturbances and poor initial parameters. The approach is validated on the `Franka Emika Panda' 7 DoF manipulator.
