PEIS stol: autonomni robotski stol za kućanstva

There are two main trends in the area of home and service robotics. The classical one aims at the development of a single skilled servant robot, able to perform complex tasks in a passive environment. The second, more recent trend aims at the achievement of complex tasks through the cooperation of a network of simpler robotic devices pervasively embedded in the domestic environment. This paper contributes to the latter trend by describing the PEIS Table, an autonomous robotic table that can be embedded in a smart environment. The robotic table can operate alone, performing simple point-to-point navigation, or it can collaborate with other devices in the environment to perform more complex tasks. Collaboration follows the PEIS Ecology model. The hardware and software design of the PEIS Table are guided by a set of requirements for robotic domestic furniture that differ, to some extent, from the requirements usually considered for service robots.U uslužnoj robotici i robotici za kućanstva postoje dva glavna trenda. Klasičan pristup teži razvoju jednog složenog uslužnog robota koji je sposoban izvršavati složene zadatke u pasivnom okruženju. Dok drugi, nešto noviji pristup, teži rješavanju složenih zadataka kroz suradnju umreženih nešto jednostavnijih robota prožetih kroz cijelo kućanstvo. Ovaj članak svoj doprinos daje drugom pristupu opisujući PEIS stol, autonomni robotski stol koji se može postaviti u inteligentnom okruženju. Robotski stol može djelovati samostalno, navigirajući od točke do točke ili može surađivati s ostalim uređajima u okruženju radi izvršavanja složenijih zadataka. Ta suradnja prati PEIS ekološki model. Dizajn sklopovlja i programske podrške PEIS stola prati zahtjeve za robotsko pokućstvo koji se donekle razlikuju od zahtjeva koji se inače postavljaju za uslužne robote

PEIS stol: autonomni robotski stol za kućanstva

There are two main trends in the area of home and service robotics. The classical one aims at the development of a single skilled servant robot, able to perform complex tasks in a passive environment. The second, more recent trend aims at the achievement of complex tasks through the cooperation of a network of simpler robotic devices pervasively embedded in the domestic environment. This paper contributes to the latter trend by describing the PEIS Table, an autonomous robotic table that can be embedded in a smart environment. The robotic table can operate alone, performing simple point-to-point navigation, or it can collaborate with other devices in the environment to perform more complex tasks. Collaboration follows the PEIS Ecology model. The hardware and software design of the PEIS Table are guided by a set of requirements for robotic domestic furniture that differ, to some extent, from the requirements usually considered for service robots.U uslužnoj robotici i robotici za kućanstva postoje dva glavna trenda. Klasičan pristup teži razvoju jednog složenog uslužnog robota koji je sposoban izvršavati složene zadatke u pasivnom okruženju. Dok drugi, nešto noviji pristup, teži rješavanju složenih zadataka kroz suradnju umreženih nešto jednostavnijih robota prožetih kroz cijelo kućanstvo. Ovaj članak svoj doprinos daje drugom pristupu opisujući PEIS stol, autonomni robotski stol koji se može postaviti u inteligentnom okruženju. Robotski stol može djelovati samostalno, navigirajući od točke do točke ili može surađivati s ostalim uređajima u okruženju radi izvršavanja složenijih zadataka. Ta suradnja prati PEIS ekološki model. Dizajn sklopovlja i programske podrške PEIS stola prati zahtjeve za robotsko pokućstvo koji se donekle razlikuju od zahtjeva koji se inače postavljaju za uslužne robote

Giustizia e letteratura II

The book explores and links different cultures, disciplines and perspectives, with a far more original and broad approach to the relations between “Justice” and “Literature” than more traditional works focused on “Law” and “Literature”. The many contributions from writers, literature and movie critics, psychologists, and criminal law practitioners and scholars, draw a complex and interdisciplinary path through primary texts of Italian and international literature, with the aim of prompting readers’ reflections about core issues related to law, crime, and responsibility. Through the analysis of masterpieces of literature, theatre and cinema, this book aims at stimulating dialogue and debate, as well as critical abilities and a deep-rooted sense of justice, amongst both law professionals and citizens at large. Literature and other forms of narration are presented here as a privileged key to approach long-standing questions about (amongst other) causes and consequences of crime; victimization and coping mechanisms; the role of criminal law and criminal proceedings; legalism and equity; law and ethics; the ‘time’ of justice; freedom, responsibility, culpability and forgiveness; rules, legality, socialization and culture; language and images as mediums for justice issues; the impact of prejudice and of existing balances of power on the application of the law; social and legal mechanisms of exclusion and inclusion; gender issues and legal systems; and so on. A whole section (Part V) is devoted to crimes against humanity and how the literary testimony may be understood both as a strategy to resist injustice and to seek justice, and as a way to prevent further horrors. Through this quest for justice in literature and arts, the volume proposes a wider cultural and research project which defies traditional formalistic and retributive approaches to criminal law, in order to open new perspectives for restorative and reintegrative strategies

Bayesian Time-Series Models: Expert Knowledge-Driven Inference and Learning for Engineering Applications

Sequential data (a.k.a. time-series) a rise in a multitude of different fields such as bi oinformatics, automatic speech recognition, roboti cs, computer vision, and computational finance. Wh ile the nature of the data generated in these appl ications is highly heterogeneous, the problems ari sing from such a wide range of fields reveal a str iking similarity when seen through the lens of pro babilistic time-series models. Most of the applica tion-specific problems fit in fact into one of the two main categories of algorithms developed for probabilistic time- series models: in ference (e.g. predict future values, estimate hidden variables from observab le ones, or partition a complex time-series in a s et of elementary segments) and learning (e.g. estimate time-series model para meters from observations, or discover pattern s or anomalies in time-series). In this thesis, a Bayesian treatment o f the uncertainties involved in time-ser ies models is adopted, and a set of probabili stic time-series models (that fall in the cat egory of switching models) is applied to four seemingly distant problems, namely¨ automatic segmentation of human gait time -series, classification of pathological g ait patterns, fault detection and recogni tion in robotic assembly tasks, and gas concentration estimation in unstructured envir onments using metal-oxide sensors. All these problems can be solved as inference or learning in a Bayesian time-series model, and have in common the crucial role that dom ain-specific expert knowledge plays in their¨ solution. The most attractive feat ure offered by the Bayesian framework is perh aps its potential to incorporate domain-s pecific expert knowledge either in the f orm of informative priors on the mod el parameters (to regularize the learning process) , or in terms of tailored conditional independ ence assumptions between model variables (to simplify inference). The main goal of this thesis is to investigate to whic h extent this potential can be fulfilled, and to h ighlight the scientific and pragmatic challenges i dentified and/or solved during this quest. In ¨particular, two Bayesian time-series models¨ are used in this thesis: the sticky-Hiera rchical Dirichlet Process Hidden Markov Model ¨(sticky-HDP-HMM) and the Augmented¨ Switching Linear Dynamical System model (aSLD S). The sticky-HDP-HMM is used in the gait segmentation and gait pattern cl assification applications. In both applications, r esults show that combining the HDP-HMM to an ad-hoc pre-processing step of the ga it time-series allows to transfer clinical kn owledge about human gait into the time-series model. This combined approach improves the p erformances in both applications, with respec t to the use of the HDP-HMM in isol ation. Motivated by these results, an extensi on of the HDP-HMM, named POLY-HDP-HMM is prop osed in this thesis. The sticky-HDP -HMM is also used for fault detection and recognit ion in robotic assembly tasks to learn force/torqu e sensor signature models. These mod els are used to classify faulty and succ essful task executions, allowing the robot to react on-line to errors and undertake er ror-specific recovery strategies. Finally, t he problem of estimating gas concentration in ¨unstructured envi- ronments using MOX sensor s is formulated as inference in an aSLDS model. In this application, domain-specific¨ expert knowledge is directly used in the construct ion of the model itself, rather than in estimating ¨its parameters. The proposed aSLDS model is¨ effective in overcoming the slow dynamical re sponse of MOX sensors, therefore extending their r ange of applicability in field robotics scenarios. In this thesis, we show that the adopted Bayesian time-series models can take advan tage of some of the available domain-specific expert knowledge to improve inferen ce and learning results. However, it appears¨ clear that, especially in clinical appli cations, further improvement can be achieved¨ increasing the transfer of clinical know ledge to the models. For this reason, we prop ose and discuss extensions to the time-series mode ls used in this thesis, and introduce new ones.status: publishe

Hierarchical Dirichlet Process Hidden Markov Models for abnormality detection in robotic assembly

The Hierarchical Dirichlet Process Hidden Markov model (HDP-HMM) is a Bayesian non parametric extension of the classical Hidden Markov Model (HMM) that allows to infer posterior probability over the cardinality of the hidden space, thus avoiding the necessity of cross-validation arising in standard EM training. This paper presents the application of Hierarchical Dirichlet Process Hidden Markov Models (HDP-HMM) to error detection during a robotic assembly task. Force sensor data is recorded for successful and failed task executions and man- ually labeled. An HDP-HMM is then fit to a set of training trials for each task execution outcome. We show how posteriors on the learned models could be used to recognize on-line deviation from expected behavior, thus allowing the robotic system to promptly react to task execution errors.status: publishe

Augmented Switching Linear Dynamical System Model for Gas Concentration Estimation with MOX Sensors in an Open Sampling System

In this paper, we introduce a Bayesian time series model approach for gas concentration estimation using Metal Oxide (MOX) sensors in Open Sampling System (OSS). Our approach focuses on the compensation of the slow response of MOX sensors, while concurrently solving the problem of estimating the gas concentration in OSS. The proposed Augmented Switching Linear System model allows to include all the sources of uncertainty arising at each step of the problem in a single coherent probabilistic formulation. In particular, the problem of detecting on-line the current sensor dynamical regime and estimating the underlying gas concentration under environmental disturbances and noisy measurements is formulated and solved as a statistical inference problem. Our model improves, with respect to the state of the art, where system modeling approaches have been already introduced, but only provided an indirect relative measures proportional to the gas concentration and the problem of modeling uncertainty was ignored. Our approach is validated experimentally and the performances in terms of speed of and quality of the gas concentration estimation are compared with the ones obtained using a photo-ionization detector

Bayesian Time-Series Models for Continuous Fault Detection and Recognition in Industrial Robotic Tasks

This paper presents the application of a Bayesian nonparametric time-series model to process monitoring and fault classification for industrial robotic tasks. By means of an alignment task performed with a real robot, we show how the proposed approach allows to learn a set of sensor signature models encoding the spatial and temporal correlations among wrench measurements recorded during a number of successful task executions. Using these models, it is possible to detect continuously and on-line deviations from the expected sensor readings. Separate models are learned for a set of possible error scenarios involving a human modifying the workspace configuration. These non-nominal task executions are correctly detected and classified with an on-line algorithm, which opens the possibility for the development of error-specific recovery strategies. Our work is complementary to previous approaches in robotics, where process monitors based on probabilistic models, but limited to contact events, were developed for control purposes. Instead, in this paper we focus on capturing dynamic models of sensor signatures throughout the whole task, therefore allowing continuous monitoring and extending the system ability to interpret and react to errors.status: publishe

Bayesian Hidden Markov Models for Segmentation of Gait Motion Capture Data

The interpretation of 3DGA data often requires the segmentation of joint angle time­series (e.g. the identification of rockers in the ankle sagittal plane kinematics). We introduce a novel method for the automatic segmentation of joint angle time­series based on a Bayesian time­series model called Hierachical Dirichlet Process Hidden Markov Model (HDP­HMM) [1]. The goal of the method is to segment a joint angle time­series in a set of piecewise polynomial curves, while at the same time estimating the curve parameters and the time­correlation between the segments. The proposed method is suited for segmention of a set of time­series, (e.g. , a set of gait cycles exhibiting a specific pattern identified by a clinician) and to learn a probabilistic shape signature that can be used for classification of gait trials. Due to its Bayesian nature, the proposed method is able to incorporate clinical prior knowledge to produce a clinically meaningful segmentation.status: publishe

Identification of Gait Events Combining Bayesian Hidden Markov Models and Linear Regression

The Hidden Markov Model is a probabilistic time- series model that has recently found application in human motion analysis. HMMs are usually fit directly to time-series data obtained from motion capture systems, using Gaussian ob- servation models and the Expectation Maximization algorithm. The boundaries of the segmentation induced by the HMM are somewhat arbitrary, because the motion capture data usually consists of smooth trajectories. When a-priori segmentation is available, like in the case of clinically defined events in human gait, biasing the HMM parameters towards this prior knowledge is crucial to obtain a segmentation that is clinically relevant. To achieve this goal, we propose the combination of a fully Bayesian HMM with a sliding-window polynomial fit pre-processing step. In the context of automatic segmentation of gait time-series, we show how the proposed approach allows to better exploit a-priori segmentation, and to learn a set of motion primitives that improve the segmentation performances over classical HMMs.status: publishe