Deep neural network (DNN)-based adaptive controllers can be used to
compensate for unstructured uncertainties in nonlinear dynamic systems.
However, DNNs are also very susceptible to overfitting and co-adaptation.
Dropout regularization is an approach where nodes are randomly dropped during
training to alleviate issues such as overfitting and co-adaptation. In this
paper, a dropout DNN-based adaptive controller is developed. The developed
dropout technique allows the deactivation of weights that are stochastically
selected for each individual layer within the DNN. Simultaneously, a
Lyapunov-based real-time weight adaptation law is introduced to update the
weights of all layers of the DNN for online unsupervised learning. A non-smooth
Lyapunov-based stability analysis is performed to ensure asymptotic convergence
of the tracking error. Simulation results of the developed dropout DNN-based
adaptive controller indicate a 38.32% improvement in the tracking error, a
53.67% improvement in the function approximation error, and 50.44% lower
control effort when compared to a baseline adaptive DNN-based controller
without dropout regularization.

本研究开发了一种基于 dropout 的深度神经网络自适应控制器，该控制器通过随机选择每个层内的权重进行随机失活，并引入基于 Lyapunov 的实时权重调整规律来实现在线无监督学习。经过非光滑的 Lyapunov 稳定性分析，该控制器保证了跟踪误差的渐近收敛。与无 dropout 正则化的自适应深度神经网络控制器相比，模拟结果显示跟踪误差提高了 38.32%，函数逼近误差改善了 53.67%，控制效果降低了 50.44%。

基于李雅普诺夫的辍学深度神经网络 (简称 Lb-DDNN) 控制器

Lyapunov-Based Dropout Deep Neural Network (Lb-DDNN) Controller

Generalized in-hand manipulation has long been an unsolved challenge of
robotics. As a small step towards this grand goal, we demonstrate how to design
and learn a simple adaptive controller to achieve in-hand object rotation using
only fingertips. The controller is trained entirely in simulation on only
cylindrical objects, which then - without any fine-tuning - can be directly
deployed to a real robot hand to rotate dozens of objects with diverse sizes,
shapes, and weights over the z-axis. This is achieved via rapid online
adaptation of the controller to the object properties using only proprioception
history. Furthermore, natural and stable finger gaits automatically emerge from
training the control policy via reinforcement learning. Code and more videos
are available at this https URL

本文介绍了使用自适应控制器和动态调整策略训练的机器人手指智能旋转物体的方法，该方法只需要使用手指就可以对各种大小、形状和重量的物体进行旋转，具有很好的稳定性和自适应性，为机器人手部操作的研究提供了一个新思路。

通过快速电机适应实现手中物体的旋转

In-Hand Object Rotation via Rapid Motor Adaptation

This paper proposes a universal adaptive controller for quadcopters, which
can be deployed zero-shot to quadcopters of very different mass, arm lengths
and motor constants, and also shows rapid adaptation to unknown disturbances
during runtime. The core algorithmic idea is to learn a single policy that can
adapt online at test time not only to the disturbances applied to the drone,
but also to the robot dynamics and hardware in the same framework. We achieve
this by training a neural network to estimate a latent representation of the
robot and environment parameters, which is used to condition the behaviour of
the controller, also represented as a neural network. We train both networks
exclusively in simulation with the goal of flying the quadcopters to goal
positions and avoiding crashes to the ground. We directly deploy the same
controller trained in the simulation without any modifications on two
quadcopters with differences in mass, inertia, and maximum motor speed of up to
4 times. In addition, we show rapid adaptation to sudden and large disturbances
(up to 35.7%) in the mass and inertia of the quadcopters. We perform an
extensive evaluation in both simulation and the physical world, where we
outperform a state-of-the-art learning-based adaptive controller and a
traditional PID controller specifically tuned to each platform individually.
Video results can be found at
this https URL.

本论文提出了一种适用于四轴飞行器的通用自适应控制器，基于神经网络，通过在仿真环境中训练实现快速适应各种扰动和硬件变化。