Learning in Robotics

Mosavi, Amir; Varkonyi, Annamaria

doi:10.5120/ijca2017911661

Cited by 26 publications

(23 citation statements)

References 48 publications

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“…One possible interpretation is that local methods had an advantage because the experiments were limited to data from two similarly configured pendulums. Another interpretation is that parts of the manifolds collapsed, i.e., they were projected onto line segments during the manifold lear- Traditional robotic motion control engines often rely on inverse kinematics and can be difficult to adapt when the robot configuration changes [15]. Chalodhorn and Rao found that direct use of kinematics data from a human motion capture system to replicate human movement in a robot can result in dynamically unstable motion [5].…”

Section: Discussionmentioning

confidence: 99%

Aligning Manifolds of Double Pendulum Dynamics Under the Influence of Noise

Aziz

Wong

Welsh

et al. 2018

Neural Information Processing

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This study presents the results of a series of simulation experiments that evaluate and compare four different manifold alignment methods under the influence of noise. The data was created by simulating the dynamics of two slightly different double pendulums in three-dimensional space. The method of semi-supervised feature-level manifold alignment using global distance resulted in the most convincing visualisations. However, the semi-supervised feature-level local alignment methods resulted in smaller alignment errors. These local alignment methods were also more robust to noise and faster than the other methods.

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Section: Discussionmentioning

confidence: 99%

Aligning Manifolds of Double Pendulum Dynamics Under the Influence of Noise

Aziz

Wong

Welsh

et al. 2018

Neural Information Processing

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“…In 2009, an automatic speech recognition application was carried out to decrease the Phone Error Rate (PER) by using two different architectures of deep belief networks [18]. In 2012, CNN [25] method was applied within the framework of a Hybrid Neural Network -Hidden Markov Model (NN -HMM). As a result, a PER of 20.07 % was achieved.…”

Section: Biometricsmentioning

confidence: 99%

Deep Learning: A Review

Vargas¹,

Mosavi²,

Ruíz³

2018

Preprint

Self Cite

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Deep learning is an emerging area of machine learning (ML) research. It comprises multiple hidden layers of artificial neural networks. The deep learn- ing methodology applies nonlinear transformations and model abstractions of high level in large databases. The recent advancements in deep learning architec- tures within numerous fields have already provided significant contributions in artificial intelligence. This article presents a state of the art survey on the contri- butions and the novel applications of deep learning. The following review chron- ologically presents how and in what major applications deep learning algorithms have been utilized. Furthermore, the superior and beneficial of the deep learning methodology and its hierarchy in layers and nonlinear operations are presented and compared with the more conventional algorithms in the common applica- tions. The state of the art survey further provides a general overview on the novel concept and the ever-increasing advantages and popularity of deep learning.

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“…Machine learning, a hot topic nowadays, has been developed for many years and used in various industrial applications [13]. Machine learning has proved its ability to control a system without the knowledge of that system (e.g., self-learning cars [14] and robotics [15,16]). However, applications for landing gear are relatively rare.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Control Based on a Neural Network for Aircraft Landing Gear with a Magnetorheological Damper in Different Landing Scenarios

2020

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A typical oleo-pneumatic shock-absorbing strut (classic traditional passive damper) in aircraft landing gear has a metering pin extending through the orifice, which can vary the orifice area with the compression and extension of the damper strut. Because the metering pin is designed in a single landing condition, the traditional passive damper cannot adjust its damping force in multiple landing conditions. Magnetorheological (MR) dampers have been receiving significant attention as an alternative to traditional passive dampers. An MR damper, which is a typical semi-active suspension system, can control the damping force created by MR fluid under the magnetic field. Thus, it can be controlled by electric current. This paper adopts a neural network controller trained by two different methods, which are genetic algorithm and policy gradient estimation, for aircraft landing gear with an MR damper that considers different landing scenarios. The controller learns from a large number of trials, and accordingly, the main advantage is that it runs autonomously without requiring system knowledge. Moreover, comparative numerical simulations are executed with a passive damper and adaptive hybrid controller under various aircraft masses and sink speeds for verifying the effectiveness of the proposed controller. The main simulation results show that the proposed controller exhibits comparable performance to the adaptive hybrid controller without any needs for the online estimation of landing conditions.

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Learning in Robotics

Cited by 26 publications

References 48 publications

Aligning Manifolds of Double Pendulum Dynamics Under the Influence of Noise

Aligning Manifolds of Double Pendulum Dynamics Under the Influence of Noise

Deep Learning: A Review

Intelligent Control Based on a Neural Network for Aircraft Landing Gear with a Magnetorheological Damper in Different Landing Scenarios

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