Universidad Politécnica de Cartagena TESIS DOCTORAL “UNA ...

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.