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Barking Robots +	Aim of this project is the development of a robot that can operate autonomously at exhibitions and malls to attract people to a given location, by showing interesting behaviors and interacting with people. The robot first exhibition has been at Robotica 2009, within HI-Tech Expo at Fiera di Milano, on November 23-25, 2009. Here, the robot had to go around in an area delimited by a white stripe and contact verbally and with gestures people entering the area, in order to attract them to the booth. Behaviors and gestures have still to be developed to come to an interesting and robust demo at next Robotica, or at other ehibits (e.g. at the Museo della Scienza of Milan).
BasketBot +	Development of a robotic basket
Behavior recognition from visual data +	In the literature several approaches have been used to model observed behaviors and these date back to early approaches in animal behavior analysis (Baum and Eagon, 1967)(Colgan, 1978). Nowadays several techniques are used and they can be roughly classified as: State space models, Automata (e.g., Finite State Machines, Agents, etc.), Grammars (e.g., strings, T-Patterns, etc.), Bayeasian models (e.g., Hidden Markov Models), and Dynamic State Variables. The work will leverage on a huge corpus of techniques to devise the most suitable for behavior recognition from visual data. We exclude from the very beginning any deterministic approach being the phenomenon under observation complex and affected by noisy observations. The focus will be mainly of the use of dynamic graphical models (Ghahramani, 1998) and the application of bottom up learning techniques (Stolcke and Omohundro, 1993)(Stolcke and Omohundro, 1994) for model induction. *L. E. Baum and J. A. Eagon. An inequality with applications to statistical estimation for probabilistic functions of markov processes and to a model for ecology. Bull. Amer. Math. Soc, 73(73):360–363, 1967. *P. W. Colgan. Quantitative Ethology. John Wiley & Sons, New York, 1978. *A. Stolcke and S. M. Omohundro. Hidden markov model induction by bayesian model merging. In Stephen Jos é Hanson, Jack D. Cowan, and C. Lee Giles, editors, Advances in Neural Information Processing Systems, volume 5. Morgan Kaufmann, San Mateo, CA, 1993. *Zoubin Ghahramani. Learning dynamic bayesian networks. Lecture Notes in Computer Science, 1387:168, 1998. *A. Stolcke and S. M. Omohundro. Best-first model merging for hidden markov model induction. Technical Report TR-94-003, 1947 Center Street, Berkeley, CA, 1994. '''''Material:''''' *papers from major journals and conferences *kinet SDK for the extraction of body poses '''''Expected outcome:''''' *general framework for the recognition of behaviors from time series *toolkit for behavior segmentation and recognition from time series *running prototype based on data coming from the Microsoft kinect sensor '''''Required skills or skills to be acquired:''''' *understanding of techniques for behavior recognition *background on pattern recognition and stochastic models *basic understanding of computer vision *C++ programming under Linux or Matlab
BinaryTags +	Aim of the project is the design and implementation of a semantic extension of the wiki system JSPWiki, to allow users annotate links between pages with tags; binary tags may then be turned into semantic relationhips and added to the substanding ontology, with the help of a suggestion tool.
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C-SLAM +	Development of a Cognitive SLAM system
CAN Bus bootloader for STM32 microcontrollers +	JOINT PROJECT with the Embedded Systems group (contact: Patrick Bellasi http://home.dei.polimi.it/bellasi/) In order to speed up the development and the maintenance of embedded applications, a way to update the firmware on a microcontroller without the need of connecting cables or programmers can be very handy. We are developing a framework for rapid prototyping of low-cost robots, with smart devices that exchange data on a CAN bus network. The CAN bus bootloader is one of the components we need for this project, enabling remote firmware upgrades of all the devices connected to the CAN network. This project aims to develop a CAN bus bootloader for STM32 ARM Cortex-M3 microcontrollers, and eventually for other architectures.
CPG for Warugadar +	Study Central Pattern Generation, develop a CPG implementation in Matlab or Python. Adapt the method to a quadruped robot (Warugadar).
Cestino +	A trash basket with a character
Characterization of the NIA signal +	The NIA system by OCZ provides a very cheap way to get a signal that includes EOG, EMG and EEG. Aim of this project is to characterize it and investigate how could it be used as a substitute of a clinical EEG for non-clinical applications, and as one more signal for affective computing.
Cognitive SLAM +	We have developed a system that is able to detect, recognize and track objects in an image taken from a low cost robot equipped with a IMU and a low cost camera. The system is capable to detect and recognize objects using a user defined fuzzy tree classifier. However the system performance is heavily dependent on high level feature extraction, such as geometric features. The problem is non trivial due to noisy low cost camera and changes in the light conditions. The aim of this project is to improve the feature extraction and description process, both in performance and quality, possible adding a more complete description or others type of features. The long term aim of the research is to have an autonomuos robot capable to create a semantic map of the envirorment, localize himself , make inference on the map, navigate into the envirorment using the objects as landmarks. No special skills are required, except basic c and object oriented programming.
Combinatorial optimization based on stochastic relaxation +	The project will focus on the study, implementation, comparison and analysis of different algorithms for the optimization of pseudo-Boolean functions, i.e., functions defined over binary variables with values in R. These functions have been studied a lot in the mathematical programming literature, and different algorithms have been proposed (1). More recently, the same problems have been faced in evolutionary computations, with the use of genetic algorithms, and in particular estimation of distribution algorithms (2,3). Estimation of distribution algorithms are a recent meta-heuristic, where classical crossover and mutation operators used in genetic algorithms are replaced with operators that come from statistics, such as sampling and estimation. The focus will be on the implementation of a new algorithm able to combine different approaches (estimation and sampling, from one side, and exploitation of prior knowledge about the structure of problem, on the other), together with the comparison of the results with existing techniques that historically appear in different (and often separated) communities. Good coding (C/C++) abilities are required. Since the approach will be based on statistical models, the student is supposed to be comfortable with notions that come from probability and statistics courses. The project could require some extra effort in order to build and consolidate some background in math, especially in Bayesian statistics and MCMC techniques, such as Gibbs and Metropolis samplers (4). The project can be extended to master thesis, according to interesting and novel directions of research that will emerge in the first part of the work. Possible ideas may concern the proposal of new algorithms able to learn existing dependencies among the variables in the function to be optimized, and exploit them in order to increase the probability to converge to the global optimum. Picture taken from http://www.ra.cs.uni-tuebingen.de/ Bibliography # Boros, Endre and Boros, Endre and Hammer, Peter L. (2002) Pseudo-boolean optimization. Discrete Applied Mathematics. # Pelikan, Martin; Goldberg, David; Lobo, Fernando (1999), A Survey of Optimization by Building and Using Probabilistic Models, Illinois: Illinois Genetic Algorithms Laboratory (IlliGAL), University of Illinois at Urbana-Champaign. # Larrañga, Pedro; & Lozano, Jose A. (Eds.). Estimation of distribution algorithms: A new tool for evolutionary computation. Kluwer Academic Publishers, Boston, 2002. # Image Analysis, Random Fields Markov Chain Monte Carlo Methods
Combining Estimation of Distribution Algorithms and other Evolutionary techniques for combinatorial optimization +	The project will focus on the study, implementation, comparison and analysis of different algorithms for combinatorial optimization using techniques and algorithms proposed in Evolutionary Computation. In particular we are interested in the study of Estimation of Distribution Algorithms (1,2,3,4), a recent meta-heuristic, often presented as an evolution of Genetic Algorithms, where classical crossover and mutation operators, used in genetic algorithms, are replaced with operators that come from statistics, such as sampling and estimation. The focus will be on the implementation of new hybrid algorithms able to combine estimation of distribution algorithms with different approaches available in the evolutionary computation literature, such as genetic algorithms and evolutionary strategies, together with other local search techniques. Good coding (C/C++) abilities are required. Some background in combinatorial optimization form the "Fondamenti di Ricerca Operativa" is desirable. The project could require some effort in order to build and consolidate some background in MCMC techniques, such as Gibbs and Metropolis samplers (4). The project could be extended to master thesis, according to interesting and novel directions of research that will emerge in the first part of the work. Computer vision provides a large number of optimization problems, such as new-view synthesis, image segmentation, panorama stitching and texture restoration, among the others, (6). One common approach in this context is based on the use of binary Markov Random Fields and on the formalization of the optimization problem as the minimum of an energy function expressed as a square-free polynomial, (5). We are interested in the proposal, comparison and evaluation of different Estimation of Distribution Algorithms for solving real world problems that appear in computer vision. Pictures taken from http://www.genetic-programming.org and (6) Bibliography # Pelikan, Martin; Goldberg, David; Lobo, Fernando (1999), A Survey of Optimization by Building and Using Probabilistic Models, Illinois: Illinois Genetic Algorithms Laboratory (IlliGAL), University of Illinois at Urbana-Champaign. # Larrañga, Pedro; & Lozano, Jose A. (Eds.). Estimation of distribution algorithms: A new tool for evolutionary computation. Kluwer Academic Publishers, Boston, 2002. # Lozano, J. A.; Larrañga, P.; Inza, I.; & Bengoetxea, E. (Eds.). Towards a new evolutionary computation. Advances in estimation of distribution algorithms. Springer, 2006. # Pelikan, Martin; Sastry, Kumara; & Cantu-Paz, Erick (Eds.). Scalable optimization via probabilistic modeling: From algorithms to applications. Springer, 2006. # Image Analysis, Random Fields Markov Chain Monte Carlo Methods # Carsten Rother, Vladimir Kolmogorov, Victor Lempitsky, Martin Szummer. Optimizing Binary MRFs via Extended Roof Duality, CVPR 2007
Comparison of State of the Art Visual Odometry Systems +	Visual odometry is the estimation of camera(s) movement from a sequence of images. In case we deal with a single camera system we have Monocular Visual Odometry; in case we have more cameras we have a Stero Visual Odometry. The goal of the thesis is to review the state of the art on in visual odometry, classify existing approaches and compare their implementations (many of the algorithms have online source code available). '''Material''' *a huge corpus of literature on the topic *libraries on visual odometry (http://www.cvlibs.net/datasets/kitti/index.php) *C++ library for image processing and computer vision (OpenCV) '''Expected outcome:''' *a set of running algorithms performing visual odometry '''Required skills or skills to be acquired:''' *computer vision and 3D reconstruction *C++ programming under Linux
Concierge +	Development of an emotional concierge head
Control of Whitefinger +	This project aims to add further capabilities to Maximum One's hand
Controllo Robot Mobile Fives +	The issue of the project is to develop an advanced control for a mobile robot
Creation of new EEG training by introduction of noise +	A Brain-Computer Interface (BCI) must be trained on the individual user in order to be effective. This training phase require recording data in long sessions, which is time consuming and boring for the user. The aim of this project is to develop algorithm to create new training EEG (electroencephalography) data from existing ones, so as to speed up the training phase. *J.R. Wolpaw et al. ''Brain-computer interfaces for communication and control'' (http://tinyurl.com/yhq27pq)
Crocobot +	Game for children with physical impairments
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Data Extraction From Wikis +	Development of a Java application for the extraction of data from wikis and their reorganization inside an ontology
Data Mining in Computer Games +	Today a lot of data can be extracted from popular games. The analysis of such data allow to discover a lot of interesting information about players, the game and the interaction between the game and different type of players. Several theses and projects are available on this topic and involve different games: TORCS, Unreal Tournament and Quake Live. Please contact us for additional information. ;References * TORCS (http://torcs.sourceforge.net/) *QuakeLive (http://www.quakelive.com) *Daniele Loiacono's AI&R Lab Projects (http://home.dei.polimi.it/loiacono/uploads/Teaching/HomePage/AIRLab_AA20092010.pdf)
Deep Learning on Event-Based Cameras +	This project aims to study deep learning techniques on event-based cameras and develop algorithms to perform object recognition on those devices.
Dense 3D Reconstruction +	The goal of the project is researching state of the art techniques in the field of 3D Reconstruction
Description Logics Extensions +	This is a research about a new temporal extension for description logics - valid for fuzzy logics but for crisp ones as well, as a restriction (so it always should be). We give new definitions for some temporal operators already present in literature and introduce some new temporal operators, called "tendency operators", whose semantics describes the temporal tendency - increasing, decreasing or constant - for the membership degree concerning a given concept. In addition, we define the new "temporal quantifiers", corresponding to some natural language's temporal adverbs, as ''usually'', ''often'', ''3 times out of 5'', etc.
Designing Living Objects +	The aim of this activity is to investigate how one or more objects in an antropic environment (home, office, hospital) can be designed and implemented to have a character and to move, having nice interactions with people. The work to be done concerns the analysis, definition, design and implementation of at least one of these objects.
Detecting patterns in ontology usage +	When building a new knowledge base the reuse of existing, well known vocabularies is often desirable. However, sometimes it is not clear which ontology should be preferable or which term is best suited for a specific application. Aim of this project is to detect patterns in ontology usage by harvesting ontologies which use a given schema (i.e. Foaf or Dublin Core) and analysing how people are using them in practice. The resulting application should download ontologies from the main semantic search engines (such as http://watson.kmi.open.ac.uk/ and http://sindice.com/), parse them and calculate statistics about the terms used inside them. The tool should show these statistics, save them in an appropriate format and make them available through an API for use by external applications.

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