Structure learning of graphical models for task-oriented robot grasping

In the collective imaginaries a robot is a human like machine as any androids in science fiction. However the type of robots that you will encounter most frequently are machinery that do work that is too dangerous, boring or onerous. Most of the robots in the world are of this type. They can be found in auto, medical, manufacturing and space industries. Therefore a robot is a system that contains sensors, control systems, manipulators, power supplies and software all working together to perform a task. The development and use of such a system is an active area of research and one of the main problems is the development of interaction skills with the surrounding environment, which include the ability to grasp objects. To perform this task the robot needs to sense the environment and acquire the object informations, physical attributes that may influence a grasp. Humans can solve this grasping problem easily due to their past experiences, that is why many researchers are approaching it from a machine learning perspective finding grasp of an object using information of already known objects. But humans can select the best grasp amongst a vast repertoire not only considering the physical attributes of the object to grasp but even to obtain a certain effect. This is why in our case the study in the area of robot manipulation is focused on grasping and integrating symbolic tasks with data gained through sensors. The learning model is based on Bayesian Network to encode the statistical dependencies between the data collected by the sensors and the symbolic task. This data representation has several advantages. It allows to take into account the uncertainty of the real world, allowing to deal with sensor noise, encodes notion of causality and provides an unified network for learning. Since the network is actually implemented and based on the human expert knowledge, it is very interesting to implement an automated method to learn the structure as in the future more tasks and object features can be introduced and a complex network design based only on human expert knowledge can become unreliable. Since structure learning algorithms presents some weaknesses, the goal of this thesis is to analyze real data used in the network modeled by the human expert, implement a feasible structure learning approach and compare the results with the network designed by the expert in order to possibly enhance it.

Genetic and morphological variation and differentiation of Raja clavata populations in the European seas: marker development and preliminary data

In this study the population structure and connectivity of the Mediterranean and Atlantic Raja clavata (L., 1758) were investigated by analyzing the genetic variation of six population samples (N = 144) at seven nuclear microsatellite loci. The genetic dataset was generated by selecting population samples available in the tissue databases of the GenoDREAM laboratory (University of Bologna) and of the Department of Life Sciences and Environment (University of Cagliari), all collected during past scientific surveys (MEDITS, GRUND) from different geographical locations in the Mediterranean basin and North-east Atlantic sea, as North Sea, Sardinian coasts, Tuscany coasts and Cyprus Island. This thesis deals with to estimate the genetic diversity and differentiation among 6 geographical samples, in particular, to assess the presence of any barrier (geographic, hydrogeological or biological) to gene flow evaluating both the genetic diversity (nucleotide diversity, observed and expected heterozygosity, Hardy- Weinberg equilibrium analysis) and population differentiation (Fst estimates, population structure analysis). In addition to molecular analysis, quantitative representation and statistical analysis of morphological individuals shape are performed using geometric morphometrics methods and statistical tests. Geometric coordinates call landmarks are fixed in 158 individuals belonging to two population samples of Raja clavata and in population samples of closely related species, Raja straeleni (cryptic sibling) and Raja asterias, to assess significant morphological differences at multiple taxonomic levels. The results obtained from the analysis of the microsatellite dataset suggested a geographic and genetic separation between populations from Central-Western and Eastern Mediterranean basins. Furthermore, the analysis also showed that there was no separation between geographic samples from North Atlantic Ocean and central-Western Mediterranean, grouping them to a panmictic population. The Landmark-based geometric morphometry method results showed significant differences of body shape able to discriminate taxa at tested levels (from species to populations).