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.

Research on the vocal culture of Orcinus orca in the Loro Parque, Tenerife: a pilot study

This research is focussed on the study of Orcinus orca's communication system. The analysis of vocalizations emitted by marine mammals has started in the '80s and most studies have been carried out in the wild. In this regard the most studied animal has been common dolphin (Tursiops truncatus) as the numerous presence of captive individuals worldwide made researches easier to be carried out. Studies about Orcinus orca's vocalizations have mainly been carried out in the wild (most in British Columbia) because its maintenance in a controlled environment results to be very difficult, only 17 among parks and oceanaria worldwide have some Orcinus orca (45 overall among which 64% born in captivity). These researches showed that Orcinus orca emit three main different types of sounds, classified as: whistles, clicks and calls. Besides, it was discovered that different groups (pods) produce sounds belonging only to the relevant pod (dialects). It is rare to find two pods sharing some calls. The two pods usually live in adjacent areas and can form a clan. This study was carried out in a controlled environment in the Orca ocean structure (Loro Parque, Tenerife, Spain) where, at the moment (March 2012) 6 individuals are hosted. Here it was developed an automatic sound recording system. Thanks to the use of suitable mathematical algorithms that allow to isolate only "interesting" sound events that differ from the "background noise", it was possible to create a database. The visualization of the sound events collected in the database is carried out with the use of a software. By looking at this output and at the observation register we could match the animal to the sound produced. Three situations were detected and studied: 1) Chosen alone: the animal chooses to go to the recording pool but it is free to move to another pool with other individuals. 2) Put alone: the animal is put alone in the recording pool. 3) With other orcas: more animals are together in the recording pool. The statistic analysis show that animals emit more vocalizations when they are in the situation "Chosen alone". The research will continue in order to observe eventual differences in the individual repertoire of each Orcinus orca.