8,444 research outputs found
A survey of machine learning techniques applied to self organizing cellular networks
In this paper, a survey of the literature of the past fifteen years involving Machine Learning (ML) algorithms applied to self organizing cellular networks is performed. In order for future networks to overcome the current limitations and address the issues of current cellular systems, it is clear that more intelligence needs to be deployed, so that a fully autonomous and flexible network can be enabled. This paper focuses on the learning perspective of Self Organizing Networks (SON) solutions and provides, not only an overview of the most common ML techniques encountered in cellular networks, but also manages to classify each paper in terms of its learning solution, while also giving some examples. The authors also classify each paper in terms of its self-organizing use-case and discuss how each proposed solution performed. In addition, a comparison between the most commonly found ML algorithms in terms of certain SON metrics is performed and general guidelines on when to choose each ML algorithm for each SON function are proposed. Lastly, this work also provides future research directions and new paradigms that the use of more robust and intelligent algorithms, together with data gathered by operators, can bring to the cellular networks domain and fully enable the concept of SON in the near future
Few-Shot Learning of Force-Based Motions From Demonstration Through Pre-training of Haptic Representation
In many contact-rich tasks, force sensing plays an essential role in adapting
the motion to the physical properties of the manipulated object. To enable
robots to capture the underlying distribution of object properties necessary
for generalising learnt manipulation tasks to unseen objects, existing Learning
from Demonstration (LfD) approaches require a large number of costly human
demonstrations. Our proposed semi-supervised LfD approach decouples the learnt
model into an haptic representation encoder and a motion generation decoder.
This enables us to pre-train the first using large amount of unsupervised data,
easily accessible, while using few-shot LfD to train the second, leveraging the
benefits of learning skills from humans. We validate the approach on the wiping
task using sponges with different stiffness and surface friction. Our results
demonstrate that pre-training significantly improves the ability of the LfD
model to recognise physical properties and generate desired wiping motions for
unseen sponges, outperforming the LfD method without pre-training. We validate
the motion generated by our semi-supervised LfD model on the physical robot
hardware using the KUKA iiwa robot arm. We also validate that the haptic
representation encoder, pre-trained in simulation, captures the properties of
real objects, explaining its contribution to improving the generalisation of
the downstream task
Derivative-free online learning of inverse dynamics models
This paper discusses online algorithms for inverse dynamics modelling in
robotics. Several model classes including rigid body dynamics (RBD) models,
data-driven models and semiparametric models (which are a combination of the
previous two classes) are placed in a common framework. While model classes
used in the literature typically exploit joint velocities and accelerations,
which need to be approximated resorting to numerical differentiation schemes,
in this paper a new `derivative-free' framework is proposed that does not
require this preprocessing step. An extensive experimental study with real data
from the right arm of the iCub robot is presented, comparing different model
classes and estimation procedures, showing that the proposed `derivative-free'
methods outperform existing methodologies.Comment: 14 pages, 11 figure
A Self-Adaptive Online Brain Machine Interface of a Humanoid Robot through a General Type-2 Fuzzy Inference System
This paper presents a self-adaptive general type-2 fuzzy inference system (GT2 FIS) for online motor imagery (MI) decoding to build a brain-machine interface (BMI) and navigate a bi-pedal humanoid robot in a real experiment, using EEG brain recordings only. GT2 FISs are applied to BMI for the first time in this study. We also account for several constraints commonly associated with BMI in real practice: 1) maximum number of electroencephalography (EEG) channels is limited and fixed, 2) no possibility of performing repeated user training sessions, and 3) desirable use of unsupervised and low complexity features extraction methods. The novel learning method presented in this paper consists of a self-adaptive GT2 FIS that can both incrementally update its parameters and evolve (a.k.a. self-adapt) its structure via creation, fusion and scaling of the fuzzy system rules in an online BMI experiment with a real robot. The structure identification is based on an online GT2 Gath-Geva algorithm where every MI decoding class can be represented by multiple fuzzy rules (models). The effectiveness of the proposed method is demonstrated in a detailed BMI experiment where 15 untrained users were able to accurately interface with a humanoid robot, in a single thirty-minute experiment, using signals from six EEG electrodes only
Computational Language Assessment in patients with speech, language, and communication impairments
Speech, language, and communication symptoms enable the early detection,
diagnosis, treatment planning, and monitoring of neurocognitive disease
progression. Nevertheless, traditional manual neurologic assessment, the speech
and language evaluation standard, is time-consuming and resource-intensive for
clinicians. We argue that Computational Language Assessment (C.L.A.) is an
improvement over conventional manual neurological assessment. Using machine
learning, natural language processing, and signal processing, C.L.A. provides a
neuro-cognitive evaluation of speech, language, and communication in elderly
and high-risk individuals for dementia. ii. facilitates the diagnosis,
prognosis, and therapy efficacy in at-risk and language-impaired populations;
and iii. allows easier extensibility to assess patients from a wide range of
languages. Also, C.L.A. employs Artificial Intelligence models to inform theory
on the relationship between language symptoms and their neural bases. It
significantly advances our ability to optimize the prevention and treatment of
elderly individuals with communication disorders, allowing them to age
gracefully with social engagement.Comment: 36 pages, 2 figures, to be submite
CasIL: Cognizing and Imitating Skills via a Dual Cognition-Action Architecture
Enabling robots to effectively imitate expert skills in longhorizon tasks
such as locomotion, manipulation, and more, poses a long-standing challenge.
Existing imitation learning (IL) approaches for robots still grapple with
sub-optimal performance in complex tasks. In this paper, we consider how this
challenge can be addressed within the human cognitive priors. Heuristically, we
extend the usual notion of action to a dual Cognition (high-level)-Action
(low-level) architecture by introducing intuitive human cognitive priors, and
propose a novel skill IL framework through human-robot interaction, called
Cognition-Action-based Skill Imitation Learning (CasIL), for the robotic agent
to effectively cognize and imitate the critical skills from raw visual
demonstrations. CasIL enables both cognition and action imitation, while
high-level skill cognition explicitly guides low-level primitive actions,
providing robustness and reliability to the entire skill IL process. We
evaluated our method on MuJoCo and RLBench benchmarks, as well as on the
obstacle avoidance and point-goal navigation tasks for quadrupedal robot
locomotion. Experimental results show that our CasIL consistently achieves
competitive and robust skill imitation capability compared to other
counterparts in a variety of long-horizon robotic tasks
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