16,622 research outputs found
Computational intelligence approaches to robotics, automation, and control [Volume guest editors]
No abstract available
Towards adaptive and autonomous humanoid robots: from vision to actions
Although robotics research has seen advances over the last decades robots are still not in widespread use outside industrial applications. Yet a range of proposed scenarios have robots working together, helping and coexisting with humans in daily life. In all these a clear need to deal with a more unstructured, changing environment arises. I herein present a system that aims to overcome the limitations of highly complex robotic systems, in terms of autonomy and adaptation. The main focus of research is to investigate the use of visual feedback for improving reaching and grasping capabilities of complex robots. To facilitate this a combined integration of computer vision and machine learning techniques is employed. From a robot vision point of view the combination of domain knowledge from both imaging processing and machine learning techniques, can expand the capabilities of robots. I present a novel framework called Cartesian Genetic Programming for Image Processing (CGP-IP). CGP-IP can be trained to detect objects in the incoming camera streams and successfully demonstrated on many different problem domains. The approach requires only a few training images (it was tested with 5 to 10 images per experiment) is fast, scalable and robust yet requires very small training sets. Additionally, it can generate human readable programs that can be further customized and tuned. While CGP-IP is a supervised-learning technique, I show an integration on the iCub, that allows for the autonomous learning of object detection and identification. Finally this dissertation includes two proof-of-concepts that integrate the motion and action sides. First, reactive reaching and grasping is shown. It allows the robot to avoid obstacles detected in the visual stream, while reaching for the intended target object. Furthermore the integration enables us to use the robot in non-static environments, i.e. the reaching is adapted on-the- fly from the visual feedback received, e.g. when an obstacle is moved into the trajectory. The second integration highlights the capabilities of these frameworks, by improving the visual detection by performing object manipulation actions
Scene understanding for autonomous robots operating in indoor environments
Mención Internacional en el título de doctorThe idea of having robots among us is not new. Great efforts are continually made to
replicate human intelligence, with the vision of having robots performing different activities,
including hazardous, repetitive, and tedious tasks. Research has demonstrated that robots are
good at many tasks that are hard for us, mainly in terms of precision, efficiency, and speed.
However, there are some tasks that humans do without much effort that are challenging for
robots. Especially robots in domestic environments are far from satisfactorily fulfilling some
tasks, mainly because these environments are unstructured, cluttered, and with a variety of
environmental conditions to control.
This thesis addresses the problem of scene understanding in the context of autonomous
robots operating in everyday human environments. Furthermore, this thesis is developed
under the HEROITEA research project that aims to develop a robot system to help
elderly people in domestic environments as an assistant. Our main objective is to develop
different methods that allow robots to acquire more information from the environment to
progressively build knowledge that allows them to improve the performance on high-level
robotic tasks. In this way, scene understanding is a broad research topic, and it is considered
a complex task due to the multiple sub-tasks that are involved. In that context, in this thesis,
we focus on three sub-tasks: object detection, scene recognition, and semantic segmentation
of the environment.
Firstly, we implement methods to recognize objects considering real indoor environments.
We applied machine learning techniques incorporating uncertainties and more modern
techniques based on deep learning. Besides, apart from detecting objects, it is essential to
comprehend the scene where they can occur. For this reason, we propose an approach
for scene recognition that considers the influence of the detected objects in the prediction
process. We demonstrate that the exiting objects and their relationships can improve the
inference about the scene class. We also consider that a scene recognition model can
benefit from the advantages of other models. We propose a multi-classifier model for scene
recognition based on weighted voting schemes. The experiments carried out in real-world
indoor environments demonstrate that the adequate combination of independent classifiers
allows obtaining a more robust and precise model for scene recognition.
Moreover, to increase the understanding of a robot about its surroundings, we propose
a new division of the environment based on regions to build a useful representation of
the environment. Object and scene information is integrated into a probabilistic fashion
generating a semantic map of the environment containing meaningful regions within each
room. The proposed system has been assessed on simulated and real-world domestic
scenarios, demonstrating its ability to generate consistent environment representations.
Lastly, full knowledge of the environment can enhance more complex robotic tasks; that is
why in this thesis, we try to study how a complete knowledge of the environment influences
the robot’s performance in high-level tasks. To do so, we select an essential task, which
is searching for objects. This mundane task can be considered a precondition to perform
many complex robotic tasks such as fetching and carrying, manipulation, user requirements,
among others. The execution of these activities by service robots needs full knowledge of
the environment to perform each task efficiently. In this thesis, we propose two searching
strategies that consider prior information, semantic representation of the environment, and
the relationships between known objects and the type of scene. All our developments are
evaluated in simulated and real-world environments, integrated with other systems, and
operating in real platforms, demonstrating their feasibility to implement in real scenarios, and
in some cases outperforming other approaches. We also demonstrate how our representation
of the environment can boost the performance of more complex robotic tasks compared to
more standard environmental representations.La idea de tener robots entre nosotros no es nueva. Continuamente se realizan grandes
esfuerzos para replicar la inteligencia humana, con la visión de tener robots que realicen
diferentes actividades, incluidas tareas peligrosas, repetitivas y tediosas. La investigación ha
demostrado que los robots son buenos en muchas tareas que resultan difíciles para nosotros,
principalmente en términos de precisión, eficiencia y velocidad. Sin embargo, existen tareas
que los humanos realizamos sin mucho esfuerzo y que son un desafío para los robots.
Especialmente, los robots en entornos domésticos están lejos de cumplir satisfactoriamente
algunas tareas, principalmente porque estos entornos no son estructurados, pueden estar
desordenados y cuentan con una gran variedad de condiciones ambientales que controlar.
Esta tesis aborda el problema de la comprensión de la escena en el contexto de robots
autónomos que operan en entornos humanos cotidianos. Asimismo, esta tesis se desarrolla
en el marco del proyecto de investigación HEROITEA que tiene como objetivo desarrollar
un sistema robótico que funcione como asistente para ayudar a personas mayores en entornos
domésticos. Nuestro principal objetivo es desarrollar diferentes métodos que permitan a
los robots adquirir más información del entorno a fin de construir progresivamente un
conocimiento que les permita mejorar su desempeño en tareas robóticas más complejas.
En este sentido, la comprensión de escenas es un tema de investigación amplio, y se
considera una tarea compleja debido a las múltiples subtareas involucradas. En esta tesis
nos enfocamos específicamente en tres subtareas: detección de objetos, reconocimiento de
escenas y etiquetado semántico del entorno.
Por un lado, implementamos métodos para el reconocimiento de objectos considerando
entornos interiores reales. Aplicamos técnicas de aprendizaje automático incorporando
incertidumbres y técnicas más modernas basadas en aprendizaje profundo. Además, aparte
de detectar objetos, es fundamental comprender la escena donde estos se encuentran. Por esta
razón, proponemos un modelo para el reconocimiento de escenas que considera la influencia
de los objetos detectados en el proceso de predicción. Demostramos que los objetos existentes
y sus relaciones pueden mejorar el proceso de inferencia de la categoría de la escena. También
consideramos que un modelo de reconocimiento de escenas puede beneficiarse de las ventajas
de otros modelos. Por ello, proponemos un multiclasificador para el reconocimiento de escenas basado en esquemas de votación ponderados. Los experimentos llevados a cabo
en entornos interiores reales demuestran que la combinación adecuada de clasificadores
independientes permite obtener un modelo más robusto y preciso para el reconocimiento
de escenas.
Adicionalmente, para aumentar la comprensión de un robot acerca de su entorno,
proponemos una nueva división del entorno basada en regiones a fin de construir una
representación útil del entorno. La información de objetos y de la escena se integra de forma
probabilística generando un mapa semántico que contiene regiones significativas dentro de
cada habitación. El sistema propuesto ha sido evaluado en entornos domésticos simulados y
reales, demostrando su capacidad para generar representaciones consistentes del entorno.
Por otro lado, el conocimiento integral del entorno puede mejorar tareas robóticas más
complejas; es por ello que en esta tesis analizamos cómo el conocimiento completo del
entorno influye en el desempeño del robot en tareas de alto nivel. Para ello, seleccionamos una
tarea fundamental, que es la búsqueda de objetos. Esta tarea mundana puede considerarse
una condición previa para realizar diversas tareas robóticas complejas, como transportar
objetos, tareas de manipulación, atender requerimientos del usuario, entre otras. La
ejecución de estas actividades por parte de robots de servicio requiere un conocimiento
profundo del entorno para realizar cada tarea de manera eficiente. En esta tesis proponemos
dos estrategias de búsqueda de objetos que consideran información previa, la representación
semántica del entorno, las relaciones entre los objetos conocidos y el tipo de escena. Todos
nuestros desarrollos son evaluados en entornos simulados y reales, integrados con otros
sistemas y operando en plataformas reales, demostrando su viabilidad de ser implementados
en escenarios reales y, en algunos casos, superando a otros enfoques. También demostramos
cómo nuestra representación del entorno puede mejorar el desempeño de tareas robóticas
más complejas en comparación con representaciones del entorno más tradicionales.Programa de Doctorado en Ingeniería Eléctrica, Electrónica y Automática por la Universidad Carlos III de MadridPresidente: Carlos Balaguer Bernaldo de Quirós.- Secretario: Fernando Matía Espada.- Vocal: Klaus Strob
Adaptive rule-based malware detection employing learning classifier systems
Efficient and accurate malware detection is increasingly becoming a necessity for society to operate. Existing malware detection systems have excellent performance in identifying known malware for which signatures are available, but poor performance in anomaly detection for zero day exploits for which signatures have not yet been made available or targeted attacks against a specific entity. The primary goal of this thesis is to provide evidence for the potential of learning classier systems to improve the accuracy of malware detection.
A customized system based on a state-of-the-art learning classier system is presented for adaptive rule-based malware detection, which combines a rule-based expert system with evolutionary algorithm based reinforcement learning, thus creating a self-training adaptive malware detection system which dynamically evolves detection rules.
This system is analyzed on a benchmark of malicious and non-malicious files. Experimental results show that the system can outperform C4.5, a well-known non-adaptive machine learning algorithm, under certain conditions. The results demonstrate the system\u27s ability to learn effective rules from repeated presentations of a tagged training set and show the degree of generalization achieved on an independent test set.
This thesis is an extension and expansion of the work published in the Security, Trust, and Privacy for Software Applications workshop in COMPSAC 2011 - the 35th Annual IEEE Signature Conference on Computer Software and Applications --Abstract, page iii
Deep learning based approaches for imitation learning.
Imitation learning refers to an agent's ability to mimic a desired behaviour by learning from observations. The field is rapidly gaining attention due to recent advances in computational and communication capabilities as well as rising demand for intelligent applications. The goal of imitation learning is to describe the desired behaviour by providing demonstrations rather than instructions. This enables agents to learn complex behaviours with general learning methods that require minimal task specific information. However, imitation learning faces many challenges. The objective of this thesis is to advance the state of the art in imitation learning by adopting deep learning methods to address two major challenges of learning from demonstrations. Firstly, representing the demonstrations in a manner that is adequate for learning. We propose novel Convolutional Neural Networks (CNN) based methods to automatically extract feature representations from raw visual demonstrations and learn to replicate the demonstrated behaviour. This alleviates the need for task specific feature extraction and provides a general learning process that is adequate for multiple problems. The second challenge is generalizing a policy over unseen situations in the training demonstrations. This is a common problem because demonstrations typically show the best way to perform a task and don't offer any information about recovering from suboptimal actions. Several methods are investigated to improve the agent's generalization ability based on its initial performance. Our contributions in this area are three fold. Firstly, we propose an active data aggregation method that queries the demonstrator in situations of low confidence. Secondly, we investigate combining learning from demonstrations and reinforcement learning. A deep reward shaping method is proposed that learns a potential reward function from demonstrations. Finally, memory architectures in deep neural networks are investigated to provide context to the agent when taking actions. Using recurrent neural networks addresses the dependency between the state-action sequences taken by the agent. The experiments are conducted in simulated environments on 2D and 3D navigation tasks that are learned from raw visual data, as well as a 2D soccer simulator. The proposed methods are compared to state of the art deep reinforcement learning methods. The results show that deep learning architectures can learn suitable representations from raw visual data and effectively map them to atomic actions. The proposed methods for addressing generalization show improvements over using supervised learning and reinforcement learning alone. The results are thoroughly analysed to identify the benefits of each approach and situations in which it is most suitable
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