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。

基于主动推理的机器人手臂新型自适应控制器

A Novel Adaptive Controller for Robot Manipulators based on Active  Inference

Transferring solutions found by trajectory optimization to robotic hardware
remains a challenging task. When the optimization fully exploits the provided
model to perform dynamic tasks, the presence of unmodeled dynamics renders the
motion infeasible on the real system. Model errors can be a result of model
simplifications, but also naturally arise when deploying the robot in
unstructured and nondeterministic environments. Predominantly, compliant
contacts and actuator dynamics lead to bandwidth limitations. While classical
control methods provide tools to synthesize controllers that are robust to a
class of model errors, such a notion is missing in modern trajectory
optimization, which is solved in the time domain. We propose frequency-shaped
cost functions to achieve robust solutions in the context of optimal control
for legged robots. Through simulation and hardware experiments we show that
motion plans can be made compatible with bandwidth limits set by actuators and
contact dynamics. The smoothness of the model predictive solutions can be
continuously tuned without compromising the feasibility of the problem.
Experiments with the quadrupedal robot ANYmal, which is driven by
highly-compliant series elastic actuators, showed significantly improved
tracking performance of the planned motion, torque, and force trajectories and
enabled the machine to walk robustly on terrain with unmodeled compliance.

通过提出基于频率的成本函数在轨迹优化中实现对于机器人模型误差的鲁棒性设计，从而增加运动规划的稳定性和兼容性。在通过模拟和硬件实验优化动作规划并在机器人 ANYmal 上进行实验后，成功地提高了机器人对于不确定环境和具有不同活动性的地形的适应性。