UNA ARQUITECTURA NEURONAL DE INSPIRACIÓN BIOLÓGICA PARA EL APRENDIZAJ

310 Pages·2010·8.98 MB·English

Universidad Politécnica de Cartagena
Departamento de Ingeniería de Sistemas y Automática

TESIS DOCTORAL
presentada en la

ESCUELA TÉCNICA SUPERIOR DE INGENIEROS
INDUSTRIALES

Para la obtención del Título de Doctor Ingeniero Industrial por la Universidad
Politécnica de Cartagena

por

Javier Molina Vilaplana

Licenciado en C.C. Físicas e Ingeniero en Electrónica

“UNA ARQUITECTURA NEURONAL DE
INSPIRACIÓN BIOLÓGICA PARA EL
APRENDIZAJE Y CONTROL DEL MOVIMIENTO
DE AGARRE EN PLATAFORMAS ROBÓTICAS
ANTROPOMORFAS”

Tesis dirigida por: Dr D Juan López Coronado

Tesis codirigida por: Dr D Jorge Feliú Batlle
Summary

Introduction, Objectives and Organization of
the Thesis.

1. Introduction
Robotics has become into a traditional field in which research is made by engineers
and scientist from different science disciplines such as mathematics, physics, medicine,
neurosciences etc. It is evident that in the last years , robotics has evolve to be a
multidisciplinary area getting closer and closer to everyday life of human beings such
as in the cases of robotics applied to rehabilitation or surgery. It also has been
established the use of robotics as a tool for the study of the Man and other biological
systems or even to construct artificial anthropomorphic components such as, arms,
sensors or cognitive – behavioural schemas able to substitute their biological
counterparts in some situations.

In the last years, it has been established within the robotics community, the idea
about that the understanding of the nervous system of humans and monkeys has also a
potential industrial or productive interest. The artificial intelligence industrial devices
are more and more inspired in Biology. The brain operates in way very different to the
way an actual robot actually operates. The mechanisms for information processing are
vastly more complex and subtle in brain neural circuits than in the electronic circuits of
the actual robots. The interactions within groups of neurons modify the properties of
neural firing of these neurons in their interaction with sensory signals from the external
world. An elemental learning such as avoiding behaviours with negative consequences,
imply millions of neuronal events, including the reconfiguration and establishment of
new neuronal connections. This is what is called ‘adaptability to environment’ of the
biological systems.
Neuro – Robotics constitutes an emergent and new field which represents, in its
objectives, a huge challenge for science ad technology: the transference of fundamental
principles of the neurobiology that drives the human behaviour to the diversity of
disciplines of the engineering that constitute the Robotics (signal processing, robust and
adaptive control, non linear systems, pattern recognition, mechatronics, etc…). If
Robotics always has been a multidisciplinary field basically at the technological level,
the need to push this field into major advances, requires a stronger interaction between
the roboticians and scientist from another fields such as neurosciences, physiology or
psychology.

Actually, neurosciences are evolving from the isolated analysis of the properties of
a neuron or small set of neurons to the analysis of the brain function at the level of
systems and subsystems. This trend allows two clear ways of interaction between
neuroscientists and engineers. On one hand, many mechanisms related with the
sensory-motor coordination, the planning or selection of behaviours in complex and
dynamical environments, or the learning and development of motor or cognitive skills
by humans, are still poorly understood by researchers. At this moment, the robotic
technology is advanced to the level that now, it is possible to construct
anthropomorphic devices that allow the experimental validation of the hypothesis and
models that there exist about the nature, properties and functioning of the neural
mechanisms mentioned above. At this level, Neuro – Robotics provides a way to reach a
deeper understanding about brain function, and eventually, could provide solutions for
the treatment of different brain dysfunctions. At the other hand, actual robots, interact
with humans in a non – flexible and non – natural way. Now, the neurosciences begin
to provide a kind of information to the systems engineer that is very useful in order to
develop models with innovative solutions for the design of new robot control
algorithms. These algorithms allow a more flexible and natural interaction between
robots and humans.

The ideas exposed above are the general reference frame in which the work of this
PhD Thesis is developed. Concretely, it could be said that, major objectives of this PhD
coincide with major objectives of two basic research projects funded by European
Commission: BRITE-SYNERAGH (Systems neuroscience and engineering research for
anthropomorphic grasping and handling, 1998-2001, BRE-2-CT980797) project and
IST/FET-PALOMA (Progressive and adaptive learning for object manipulation: a
biologically inspired multi-network architecture, 2001-2004, IST-2001-33073) project.
The author of this PhD Thesis is ascribed to NeuroTechnology, Control and Robotics
research group of the Universidad Politécnica de Cartagena. This research group and
the author of this Thesis, have been intensively involved in the development of the two
mentioned European projects.

2. Objectives of the Thesis
The scientific objectives of the Thesis are:

1) Development of new neural algorithms that mimic the interactions
between cerebral cortex and subcortical structures such as basal ganglia
during the motor-cognitive behaviour that it is manifested in grasping
tasks by human and primates.

2) Development of a modular, high level and platform non-dependent
Library of Hand Gestures (LHG). The Library of Hand Gestures can be
understood as an extension of the concept of motor schemas and finite
state machines (Arbib 1981, 1985a, 1985b, 1990). The LGH is composed by
a set of elementary motor units, each of them, codifying a concrete motor
program able to generate the adequate pattern of finger movements
associated with a concrete grasping task. The use of a LHG it specially
useful during the first phase of the movement when it is not possible to
have tactile feedback for guiding the grasping process.

3) Development of a multi-network neural architecture inspired by cortical
connectivity, that allows the progressive learning of reach to grasp tasks
by a generic robotic anthropomorphic manipulator. The learning process
has been subdivided into two main subprocess, i) Learning of the visual
effects that induces a motor command executed by the hand, ii) Learning
of the appropriate motor commands related with the object dependent
reach to grasp tasks proposed in this PhD Thesis. The main hypothesis of
this multi-network architecture consists of assuming that the correct
learning of a complex motor task such as prehension basically depends on
the neural architecture and the kind of information processing that it
induces and not on purely algorithmic solutions.

4) Transferring of biological principles of motor-cognitive behaviour of
human and primates to the design of new control algorithms for more
flexible and robust anthropomorphic robots.
The results of the research developed in this PhD Thesis could be applied in areas
in which a human – like behaviour could be needed for the robot operation, such as
advanced prosthetics, service robotics or rehabilitation robotics.

3. Organization of the Thesis
This Thesis has been organized in the following way: in Chapter 1, we describe the
more relevant aspects related with animal and human motor behavior during reach to
grasp tasks. We review the invariant properties of the reach to grasp movement,
defined by a number of experiences with humans and primates. This Chapter also
reviews the actual knowledge about the subjacent neurobiology related with the
organization of the prehension movement.

In Chapter 2, we review the computational models present in the bibliography that
have tried to explain the phenomenology (or part of it) related with the reach to grasp
movement (Haggard and Wing model, Hoff – Arbib model, Ulloa-Bullock model,
Smeets – Brenner, model). After this review is completed, we present a computational
neural model for the coordination of the reach to grasp movement. Simulation of the
model under different situations are presented and we obtain the operational properties
of the system by comparing these results with the results of similar experiences carried
out by humans. We also discuss the emergent properties (the properties that are not
explicitly taken into account during the design phase) of the model. Finally the results
of the simulation of our model are compared with the results offered by the models
named above.

In Chapter 3 we study the phenomenology associated to Parkinson’s disease in
reach to grasp movements. This review of phenomena provides the start point for the
development of a new computational neural model able to explain the properties of the
kinematic patterns of the reach to grasp movement in Normal and Parkinsonian states.
We use techniques of computational neuroscience to develop models that constitute a
plausible neural representation of the referred spatio-temporal patterns associated to
the different components of the prehension movement. We use the Parkinsonian state
as a window to test and validate the hypothesis that have lead to the models presented
in this Chapter and in the previous Chapter.

In Chapter 4, we began with the process of trying to transfer some neurobiological
principles to the design of new robotic hand controllers. This process consists in to put
the models presented in previous Chapters in a form suitable to act as advanced robotic
controllers that can be implanted easily on an anthropomorphic robot. In this Chapter, a
neural network model for the coordination of whole hand gesture during reach to grasp
movements is presented. In this model, the end effectors of the movement are
anthropomorphic. In this Chapter we also present, the results of a set of behavioural
experiences in which we have measured the coordinated kinematic patterns of motion
of the fingers during different grasping tasks. The analysis of these results have lead to
the proposal of a biologically inspired model for control of hand gesture during
prehension. The main concept of this new model is the idea of dimensionality reduction
in the control and coordination of finger movement during grasping. The model
performance is widely tested and discussed using simulation.

In Chapter 5, we introduce a multi – network architecture for the study of the
different (related with the nature of the sensory information involved) and progressive
(related to the fact that they occur at different stages of development) process of
acquiring and learning grasping skills. A modular neural network composed by a set of
neural networks with similar architecture is proposed. This system allows the
progressive learning of the different subprocess that conform a whole grasping
movement. The system, after a training process, is able to generate correct prehension
movements when objects of different types, size and orientation are presented at
different locations in the workspace. We discuss the biological plausibility of the model
and its performance is shown with simulation results.

In Chapter 6, we describe the NEUROCOR –UPCT anthropomorphic platform. In
this Chapter we also present the results of experiments carried out after the
implantation of some of the neural models for grasping developed in previous
Chapters, into the hardware-software platform that constitutes the NEUROCOR –UPCT
robotic platform.

Finally, the PhD thesis concludes with the exposition and synthesis of the most
relevant aspects obtained in this research and with the proposal to extend and develop
the results of this Thesis in the next future.
Índice General

Agradecimientos

Introducción...........................................................................................................................................................1
1. Introducción..................................................................................................................................................1
2. Objetivos de la Tesis...................................................................................................................................3
3. Metodología empleada en el desarrollo de la investigación. El Modelado Neuronal
Dinámico...........................................................................................................................................................4
3.1 Análisis experimental del sistema biológico......................................................................................5
3.2 Estudio de la neurobiología del sistema.............................................................................................6
3.3 Modelo matemático dinámico..............................................................................................................6
3.4 Simulación del modelo..........................................................................................................................6
3.5 Obtención de características operacionales........................................................................................7
3.6 Propiedades emergentes.......................................................................................................................7
3.7 Transferencia tecnológica......................................................................................................................7
4. Organización de la Tesis.............................................................................................................................7

Capítulo 1. El Movimiento de Agarre..............................................................................................................11
1. Introducción................................................................................................................................................11
2. Análisis experimental del sistema biológico........................................................................................12
2.1 Perfiles de velocidad acampanados y curvatura en las trayectorias de movimientos
de alcance....................................................................................................................................................13
2.2 Segmentación del Movimiento..........................................................................................................14
2.3 El dilema Velocidad-Precisión...........................................................................................................15
2.4 El problema de la Redundancia o de la Equivalencia Motora......................................................15
2.5 Aprendizaje Motor..............................................................................................................................17
2.6 Acoplamiento Acción-Percepción.....................................................................................................18
3. El movimiento de agarre...........................................................................................................................19
3.1 La hipótesis de los canales visuomotores en la organización del movimiento de
agarre...........................................................................................................................................................21
3.2 Descripción cinemática de Movimientos de Agarre Normales.....................................................23
3.3 Movimientos de Agarre con Perturbación. Efectos de la variación de la posición del
objeto............................................................................................................................................................26
3.3.1 Variaciones sistemáticas en la posición del objeto...........................................................................26
3.3.2 Variaciones abruptas en la posición del objeto durante la ejecución del movimiento......................26
3.4 Movimientos de Agarre con Perturbación. Efectos de la variación del tamaño del
objeto............................................................................................................................................................29
3.4.1 Variaciones sistemáticas en el tamaño del objeto.............................................................................29
3.4.2 Variaciones abruptas en el tamaño del objeto durante la ejecución del movimiento.......................29
3.4.3 El control visual del movimiento de agarre....................................................................................31
4. Transformaciones visuomotoras en el movimiento de agarre. Estudio de la
Neurobiológía del sistema.......................................................................................................................34
4.1 Circuitos visuomotores fronto – parietales......................................................................................34
4.2 Transformaciones visuomotoras relacionadas con el agarre. Área F5 (córtex
premotor PMd)...........................................................................................................................................35
Área AIP (córtex parietal posterior PPC)................................................................................................38
4.3 Transformaciones visuomotoras relacionadas con el alcance........................................................39
Área F4 (córtex premotor PMd)...............................................................................................................39
Área 7b y VIP (córtex parietal posterior, PPC)........................................................................................41
Capítulo 2. Modelos Computacionales para el Movimiento de Agarre....................................................45
1. Introducción................................................................................................................................................45
2. Modelos computacionales previos para la coordinación del movimiento de agarre...................46
2.1 El modelo de Hoff-Arbib....................................................................................................................46
2.2 El modelo de Haggard-Wing.............................................................................................................48
2.3 Modelo de Ulloa-Bullock....................................................................................................................50
2.3.1 Sincronía temporal de componentes del movimiento empleando el modelo VITE...........................50
2.3.2 Hipótesis y estructura del modelo..................................................................................................53
2.4 Modelo de Smeets-Brenner................................................................................................................55
3. Modelo Neuronal para la coordinación del movimiento de agarre.................................................57
3.1 Modelo para agarres sin perturbaciones..........................................................................................58
3.2 Modelo para agarres con perturbaciones.........................................................................................62
3.3 Simulaciones de los modelos.............................................................................................................64
3.3.1 Características básicas del movimiento de agarre...........................................................................64
3.3.2 Aperturas iniciales nulas vs grandes aperturas iniciales...............................................................65
3.3.3 Perturbación de la posición del objeto.............................................................................................70
3.3.4 Perturbación del tamaño del objeto................................................................................................71
3.3.5 Doble perturbación.........................................................................................................................73
3.3.6 Funcionamiento del modelo para un rango de amplitudes de la señal GO y de tamaños
del objeto...................................................................................................................................................74
3.4. Discusión..............................................................................................................................................78
3.4.1 Propiedades operacionales y propiedades emergentes del modelo....................................................78
3.4.2 Comparación con otros modelos......................................................................................................80
Modelo Sensomotor de Haggard y Wing.................................................................................................80
Modelo de Hoff – Arbib............................................................................................................................82
Modelo de Smeets – Brenner....................................................................................................................85
Modelo Propuesto.....................................................................................................................................87

Capítulo 3. El Movimiento de agarre en la Enfermedad de Parkinson. Modelos
Computacionales................................................................................................................................................90
1. Introducción................................................................................................................................................90
2. Movimiento de agarre y Enfermedad de Parkinson. Análisis experimental del sistema
biológico..........................................................................................................................................................91
3. Ganglios Basales. Estudio de la Neurobiología del Sistema.............................................................93
3.1 Anatomía y Funcionalidad básica de los circuitos neuronales de los Ganglios
Basales.....................................................................................................................................................93
3.2 Aspectos anatómicos del procesado de la información por los ganglios basales en
el estado normal y en el estado parkinsoniano.................................................................................97
3.3 Redes estriatales de interneuronas colinérgicas..........................................................................99
4. Modelo computacional dinámico de los circuitos de los ganglios basales..............................104
4.1 Descripción del modelo neuronal dinámico del funcionamiento de los circuitos de
los ganglios basales.............................................................................................................................106
4.2 Descripción del modelo neuronal de interacción entre distintos circuitos en los
ganglios basales mediante redes neuronales de interneuronas estriatales..................................115
5. Modelos de coordinación espacio-temporal del movimiento de agarre. Enfermedad
de Parkinson.............................................................................................................................................123
5.1 Modelo sin perturbaciones...........................................................................................................123
5.2 Simulaciones del modelo. Modelización neuronal de la disrupción temporal del
patrón motriz de agarre en la Enfermedad de Parkinson..............................................................125
Efectos de variar la distancia del objeto..................................................................................................132
Efectos de variar el tipo de agarre...........................................................................................................132
Experiencias EP. Retraso en el inicio de la componente de Agarre........................................................133
Discusión de los resultados....................................................................................................................134
5.3 Modelo con perturbaciones..........................................................................................................135
5.4 Simulaciones de los modelos. El movimiento de agarre perturbado en la EP......................137
6. Discusión sobre la plausibilidad biológica de los modelos............................................................143

Capítulo 4.Modelo Neuronal para la coordinación del gesto manual durante el Agarre....................149
1. Introducción..............................................................................................................................................149
2. Planificación de la postura de una mano antropomorfa en una tarea de agarre..........................150
3. Experimentos con CyberGlove. Análisis y Síntesis del gesto de agarre........................................153
3.1 Métodos de las experiencias.............................................................................................................155
3.1.1 Paradigma experimental y procedimientos.................................................................................155
3.1.2 Análisis de los datos.......................................................................................................................157
3.2 Resultados..........................................................................................................................................158
Análisis SVD de todos para todos los agarres y sujetos........................................................................160
3.3 Discusión de los resultados...............................................................................................................172
Evolución de la modulación de las sinergias a lo largo del movimiento de agarre.................................176
Representaciones neuronales de las autoposturas..................................................................................177
4. Modelo Neuronal para la coordinación de la preconfiguración de agarre de una mano
antropomorfa................................................................................................................................................178
4.1 Introducción. Conceptos generales que maneja el modelo..........................................................178
4.2 Modelo para el control sinérgico del movimiento de los dedos de una mano
antropomorfa. Biblioteca de Gestos.......................................................................................................182
4.3 Modelo neuronal para la formación del gesto de agarre.............................................................184
4.4 Modelo neuronal para la formación de la orientación de la palma............................................187
4.5 Modelo neuronal para el control de la componente de transporte. Modelo DIRECT.............187
4.5 Simulaciones del modelo...................................................................................................................189
4.5.1. Evolución temporal del gesto de agarre........................................................................................191
4.5.2. Coordinación entre las distintas componentes del movimiento....................................................196
4.5.3. Comportamiento del modelo ante condiciones iniciales alteradas en la apertura de la
mano.......................................................................................................................................................199
5. Discusión sobre la plausibilidad biológica del modelo...................................................................200
6. Conclusiones.............................................................................................................................................204

Capítulo 5. Modelo Neuronal para el Aprendizaje progresivo de tareas de Agarre.............................207
1. Introducción..............................................................................................................................................207
2. Modelos neuronales para el aprendizaje de tareas de alcance y agarre.........................................208
2.1 Movimientos de alcance: Modelos conexionistas..................................................................................208
2.2 Movimientos de agarre: Modelos conexionistas. Redes Neuronales para el aprendizaje de la
postura de la mano......................................................................................................................................213
Combinación de ejemplos humanos y criterios de optimización.............................................................213
Aprendizaje por refuerzo en la planificación del agarre.........................................................................215
3. Arquitectura Neuronal Multi – Red para el aprendizaje progresivo de tareas de agarre...........219
3.1 Módulo básico. Red Neuronal HYPBF............................................................................................221
Cómo sintetizar a través del aprendizaje los módulos básicos de aproximación. Redes de
regularización.........................................................................................................................................222
Aprendizaje............................................................................................................................................224
Interpretación de la red HYPBF.............................................................................................................225
3.2 Módulo de Alcance. Aprendizaje de la Cinemática Inversa del brazo manipulador...............225
3.3 Módulo de Agarre..............................................................................................................................228
3.3.1 Vector de entrada y Vector de salida del módulo GRASP.............................................................233
3.3.2 Selección heurística de los puntos de contacto sobre el objeto.......................................................235
3.3.3 Generación de trayectorias de agarre.............................................................................................236
3.3.4 Aprendizaje de la red HYPBF asociada al módulo GRASP..........................................................239
Errores cometidos en agarres con dos dedos...........................................................................................241
Errores cometidos en agarres con tres dedos..........................................................................................241
4. Simulaciones del modelo y Resultados...............................................................................................242
4.1 Entrenamiento y capacidad de generalización del modelo..........................................................242
4.1.1 Errores de aprendizaje y generalización del módulo GRASP..............................................244
4.2 Estudio de la actividad neuronal del modelo en relación a las distintas entradas...................249
4.3 Generación de movimientos completos de agarre.........................................................................253
5. Discusión de los resultados y conclusiones.......................................................................................257
5.1 El módulo GRASP y el circuito neuronal de agarre en primates................................................257
5.2 Interpretación del modelo en el contexto de la programación holística del
movimiento de agarre..............................................................................................................................263

Capítulo 6. Implantación de algoritmos sobre plataformas robóticas antropomorfas.........................266
1. Introducción..............................................................................................................................................266
2. Descripción técnica de la plataforma robótica....................................................................................267
2.1 El sistema de visión............................................................................................................................269
2.2 El brazo ABB IRB 1400.......................................................................................................................270
2.3 La mano robótica UPCT – NEUROCOR.........................................................................................270
2.4 Integración software de la plataforma NEUROCOR – GRASPING...........................................271
3. Experimentos de alcance y agarre con plataforma NEUROCOR....................................................272
3.1 Experimentos de alcance...................................................................................................................273
3.2 Experimentos de agarre.....................................................................................................................274
4. Conclusiones.............................................................................................................................................278

Capítulo 7. Conclusiones y Trabajos Futuros..............................................................................................281
1. Conclusiones de la Tesis Doctoral........................................................................................................281
2. Trabajos Futuros.......................................................................................................................................283

Apéndice

Bibliografía