121,685 research outputs found
Gaussian Processes for Machine Learning in Robotics
Mención Internacional en el título de doctorNowadays, machine learning is widely used in robotics for a variety of tasks such as
perception, control, planning, and decision making. Machine learning involves learning,
reasoning, and acting based on the data. This is achieved by constructing computer
programs that process the data, extract useful information or features, make predictions to
infer unknown properties, and suggest actions to take or decisions to make. This computer
program corresponds to a mathematical model of the data that describes the relationship
between the variables that represent the observed data and properties of interest. The
aforementioned model is learned based on the available training data, which is accomplished
using a learning algorithm capable of automatically adjusting the parameters of
the model to agree with the data. Therefore, the architecture of the model needs to be selected
accordingly, which is not a trivial task and usually depends on the machine-learning
engineer’s insights and past experience. The number of parameters to be tuned varies significantly
with the selected machine learning model, ranging from two or three parameters
for Gaussian processes (GP) to hundreds of thousands for artificial neural networks.
However, as more complex and novel robotic applications emerge, data complexity
increases and prior experience may be insufficient to define adequate mathematical models.
In addition, traditional machine learning methods are prone to problems such as
overfitting, which can lead to inaccurate predictions and catastrophic failures in critical
applications. These methods provide probabilistic distributions as model outputs, allowing
for estimating the uncertainty associated with predictions and making more informed
decisions. That is, they provide a mean and variance for the model responses.
This thesis focuses on the application of machine learning solutions based on Gaussian
processes to various problems in robotics, with the aim of improving current methods and
providing a new perspective. Key areas such as trajectory planning for unmanned aerial
vehicles (UAVs), motion planning for robotic manipulators and model identification of
nonlinear systems are addressed. In the field of path planning for UAVs, algorithms based on Gaussian processes that
allow for more efficient planning and energy savings in exploration missions have been
developed. These algorithms are compared with traditional analytical approaches, demonstrating
their superiority in terms of efficiency when using machine learning. Area coverage
and linear coverage algorithms with UAV formations are presented, as well as a
sea surface search algorithm. Finally, these algorithms are compared with a new method
that uses Gaussian processes to perform probabilistic predictions and optimise trajectory
planning, resulting in improved performance and reduced energy consumption.
Regarding motion planning for robotic manipulators, an approach based on Gaussian
process models that provides a significant reduction in computational times is proposed.
A Gaussian process model is used to approximate the configuration space of a robot,
which provides valuable information to avoid collisions and improve safety in dynamic
environments. This approach is compared to conventional collision checking methods
and its effectiveness in terms of computational time and accuracy is demonstrated. In this
application, the variance provides information about dangerous zones for the manipulator.
In terms of creating models of non-linear systems, Gaussian processes also offer significant
advantages. This approach is applied to a soft robotic arm system and UAV energy
consumption models, where experimental data is used to train Gaussian process models
that capture the relationships between system inputs and outputs. The results show accurate
identification of system parameters and the ability to make reliable future predictions.
In summary, this thesis presents a variety of applications of Gaussian processes in
robotics, from trajectory and motion planning to model identification. These machine
learning-based solutions provide probabilistic predictions and improve the ability of robots
to perform tasks safely and efficiently. Gaussian processes are positioned as a powerful
tool to address current challenges in robotics and open up new possibilities in the field.El aprendizaje automático ha revolucionado el campo de la robótica al ofrecer una amplia
gama de aplicaciones en áreas como la percepción, el control, la planificación y la toma de
decisiones. Este enfoque implica desarrollar programas informáticos que pueden procesar
datos, extraer información valiosa, realizar predicciones y ofrecer recomendaciones o
sugerencias de acciones. Estos programas se basan en modelos matemáticos que capturan
las relaciones entre las variables que representan los datos observados y las propiedades
que se desean analizar. Los modelos se entrenan utilizando algoritmos de optimización
que ajustan automáticamente los parámetros para lograr un rendimiento óptimo.
Sin embargo, a medida que surgen aplicaciones robóticas más complejas y novedosas,
la complejidad de los datos aumenta y la experiencia previa puede resultar insuficiente
para definir modelos matemáticos adecuados. Además, los métodos de aprendizaje automático
tradicionales son propensos a problemas como el sobreajuste, lo que puede llevar
a predicciones inexactas y fallos catastróficos en aplicaciones críticas. Para superar estos
desafíos, los métodos probabilísticos de aprendizaje automático, como los procesos
gaussianos, han ganado popularidad. Estos métodos ofrecen distribuciones probabilísticas
como salidas del modelo, lo que permite estimar la incertidumbre asociada a las
predicciones y tomar decisiones más informadas. Esto es, proporcionan una media y una
varianza para las respuestas del modelo.
Esta tesis se centra en la aplicación de soluciones de aprendizaje automático basadas
en procesos gaussianos a diversos problemas en robótica, con el objetivo de mejorar los
métodos actuales y proporcionar una nueva perspectiva. Se abordan áreas clave como la
planificación de trayectorias para vehículos aéreos no tripulados (UAVs), la planificación
de movimientos para manipuladores robóticos y la identificación de modelos de sistemas
no lineales.
En el campo de la planificación de trayectorias para UAVs, se han desarrollado algoritmos basados en procesos gaussianos que permiten una planificación más eficiente y
un ahorro de energía en misiones de exploración. Estos algoritmos se comparan con los
enfoques analíticos tradicionales, demostrando su superioridad en términos de eficiencia
al utilizar el aprendizaje automático. Se presentan algoritmos de recubrimiento de áreas
y recubrimiento lineal con formaciones de UAVs, así como un algoritmo de búsqueda
en superficies marinas. Finalmente, estos algoritmos se comparan con un nuevo método
que utiliza procesos gaussianos para realizar predicciones probabilísticas y optimizar la
planificación de trayectorias, lo que resulta en un rendimiento mejorado y una reducción
del consumo de energía.
En cuanto a la planificación de movimientos para manipuladores robóticos, se propone
un enfoque basado en modelos gaussianos que permite una reducción significativa
en los tiempos de cálculo. Se utiliza un modelo de procesos gaussianos para aproximar
el espacio de configuraciones de un robot, lo que proporciona información valiosa para
evitar colisiones y mejorar la seguridad en entornos dinámicos. Este enfoque se compara
con los métodos convencionales de planificación de movimientos y se demuestra su eficacia
en términos de tiempo de cálculo y precisión de los movimientos. En esta aplicación,
la varianza proporciona información sobre zonas peligrosas para el manipulador.
En cuanto a la identificación de modelos de sistemas no lineales, los procesos gaussianos
también ofrecen ventajas significativas. Este enfoque se aplica a un sistema de
brazo robótico blando y a modelos de consumo energético de UAVs, donde se utilizan
datos experimentales para entrenar un modelo de proceso gaussiano que captura las relaciones
entre las entradas y las salidas del sistema. Los resultados muestran una identificación
precisa de los parámetros del sistema y la capacidad de realizar predicciones
futuras confiables.
En resumen, esta tesis presenta una variedad de aplicaciones de procesos gaussianos
en robótica, desde la planificación de trayectorias y movimientos hasta la identificación
de modelos. Estas soluciones basadas en aprendizaje automático ofrecen predicciones
probabilísticas y mejoran la capacidad de los robots para realizar tareas de manera segura
y eficiente. Los procesos gaussianos se posicionan como una herramienta poderosa para
abordar los desafíos actuales en robótica y abrir nuevas posibilidades en el campo.Programa de Doctorado en Ingeniería Eléctrica, Electrónica y Automática por la Universidad Carlos III de MadridPresidente: Juan Jesús Romero Cardalda.- Secretaria: María Dolores Blanco Rojas.- Vocal: Giuseppe Carbon
On the Geometry of Message Passing Algorithms for Gaussian Reciprocal Processes
Reciprocal processes are acausal generalizations of Markov processes
introduced by Bernstein in 1932. In the literature, a significant amount of
attention has been focused on developing dynamical models for reciprocal
processes. Recently, probabilistic graphical models for reciprocal processes
have been provided. This opens the way to the application of efficient
inference algorithms in the machine learning literature to solve the smoothing
problem for reciprocal processes. Such algorithms are known to converge if the
underlying graph is a tree. This is not the case for a reciprocal process,
whose associated graphical model is a single loop network. The contribution of
this paper is twofold. First, we introduce belief propagation for Gaussian
reciprocal processes. Second, we establish a link between convergence analysis
of belief propagation for Gaussian reciprocal processes and stability theory
for differentially positive systems.Comment: 15 pages; Typos corrected; This paper introduces belief propagation
for Gaussian reciprocal processes and extends the convergence analysis in
arXiv:1603.04419 to the Gaussian cas
Gaussian Processes in Machine Learning
We give a basic introduction to Gaussian Process regression models. We focus on understanding the role of the stochastic process and how it is used to define a distribution over functions. We present the simple equations for incorporating training data and examine how to learn the hyperparameters using the marginal likelihood. We explain the practical advantages of Gaussian Process and end with conclusions and a look at the current trends in GP work
Vector-valued Gaussian Processes on Riemannian Manifolds via Gauge Equivariant Projected Kernels
Gaussian processes are machine learning models capable of learning unknown functions in a way that represents uncertainty, thereby facilitating construction of optimal decision-making systems. Motivated by a desire to deploy Gaussian processes in novel areas of science, a rapidly-growing line of research has focused on constructively extending these models to handle non-Euclidean domains, including Riemannian manifolds, such as spheres and tori. We propose techniques that generalize this class to model vector fields on Riemannian manifolds, which are important in a number of application areas in the physical sciences. To do so, we present a general recipe for constructing gauge equivariant kernels, which induce Gaussian vector fields, i.e. vector-valued Gaussian processes coherent with geometry, from scalar-valued Riemannian kernels. We extend standard Gaussian process training methods, such as variational inference, to this setting. This enables vector-valued Gaussian processes on Riemannian manifolds to be trained using standard methods and makes them accessible to machine learning practitioners
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