Mapping beyond what you can see: Predicting the layout of rooms behind closed doors

The availability of maps of indoor environments is often fundamental for autonomous mobile robots to efficiently operate in industrial, office, and domestic applications. When robots build such maps, some areas of interest could be inaccessible, for instance, due to closed doors. As a consequence, these areas are not represented in the maps, possibly causing limitations in robot localization and navigation. In this paper, we provide a method that completes 2D grid maps by adding the predicted layout of the rooms behind closed doors. The main idea of our approach is to exploit the underlying geometrical structure of indoor environments to estimate the shape of unobserved rooms. Results show that our method is accurate in completing maps also when large portions of environments cannot be accessed by the robot during map building. We experimentally validate the quality of the completed maps by using them to perform path planning tasks.(c) 2022 Elsevier B.V. All rights reserved

Adversarial Data Augmentation for HMM-based Anomaly Detection

In this work, we concentrate on the detection of anomalous behaviors in systems operating in the physical world and for which it is usually not possible to have a complete set of all possible anomalies in advance. We present a data augmentation and retraining approach based on adversarial learning for improving anomaly detection. In particular, we first define a method for gener- ating adversarial examples for anomaly detectors based on Hidden Markov Models (HMMs). Then, we present a data augmentation and retraining technique that uses these adversarial examples to improve anomaly detection performance. Finally, we evaluate our adversarial data augmentation and retraining approach on four datasets showing that it achieves a statistically significant perfor- mance improvement and enhances the robustness to adversarial attacks. Key differences from the state-of-the-art on adversarial data augmentation are the focus on multivariate time series (as opposed to images), the context of one-class classification (in contrast to standard multi-class classification), and the use of HMMs (in contrast to neural networks)

HMMs for Anomaly Detection in Autonomous Robots

Detection of anomalies and faults is a key element for long-term robot autonomy, because, together with subsequent diagnosis and recovery, allows to reach the required levels of robustness and persistency. In this paper, we propose an approach for detecting anomalous behaviors in autonomous robots starting from data collected during their routine operations. The main idea is to model the nominal (expected) behavior of a robot system using Hidden Markov Models (HMMs) and to evaluate how far the observed behavior is from the nominal one using variants of the Hellinger distance adopted for our purposes. We present a method for online anomaly detection that computes the Hellinger distance between the probability distribution of observations made in a sliding window and the corresponding nominal emission probability distribution. We also present a method for o!ine anomaly detection that computes a variant of the Hellinger distance between two HMMs representing nominal and observed behaviors. The use of the Hellinger distance positively impacts on both detection performance and interpretability of detected anomalies, as shown by results of experiments performed in two real-world application domains, namely, water monitoring with aquatic drones and socially assistive robots for elders living at home. In particular, our approach improves by 6% the area under the ROC curve of standard online anomaly detection methods. The capabilities of our o!ine method to discriminate anomalous behaviors in real-world applications are statistically proved

SAT based Enforcement of Domotic Effects in Smart Environments

The emergence of economically viable and efficient sensor technology provided impetus to the development of smart devices (or appliances). Modern smart environments are equipped with a multitude of smart devices and sensors, aimed at delivering intelligent services to the users of smart environments. The presence of these diverse smart devices has raised a major problem of managing environments. A rising solution to the problem is the modeling of user goals and intentions, and then interacting with the environments using user defined goals. `Domotic Effects' is a user goal modeling framework, which provides Ambient Intelligence (AmI) designers and integrators with an abstract layer that enables the definition of generic goals in a smart environment, in a declarative way, which can be used to design and develop intelligent applications. The high-level nature of domotic effects also allows the residents to program their personal space as they see fit: they can define different achievement criteria for a particular generic goal, e.g., by defining a combination of devices having some particular states, by using domain-specific custom operators. This paper describes an approach for the automatic enforcement of domotic effects in case of the Boolean application domain, suitable for intelligent monitoring and control in domotic environments. Effect enforcement is the ability to determine device configurations that can achieve a set of generic goals (domotic effects). The paper also presents an architecture to implement the enforcement of Boolean domotic effects, and results obtained from carried out experiments prove the feasibility of the proposed approach and highlight the responsiveness of the implemented effect enforcement architectur

Measuring Progress in Robotics: Benchmarking and the ‘Measure-Target Confusion’

While it is often said that in order to qualify as a true science robotics should aspire to reproducible and measurable results that allow benchmarking, I argue that a focus on benchmarking will be a hindrance for progress. Several academic disciplines that have been led into pursuing only reproducible and measurable ‘scientific’ results—robotics should be careful not to fall into that trap. Results that can be benchmarked must be specific and context-dependent, but robotics targets whole complex systems independently of a specific context—so working towards progress on the technical measure risks missing that target. It would constitute aiming for the measure rather than the target: what I call ‘measure-target confusion’. The role of benchmarking in robotics shows that the more general problem to measure progress towards more intelligent machines will not be solved by technical benchmarks; we need a balanced approach with technical benchmarks, real-life testing and qualitative judgment

To explore or to exploit? Learning humans' behaviour to maximize interactions with them

Assume a robot operating in a public space (e.g., a library, a museum) and serving visitors as a companion, a guide or an information stand. To do that, the robot has to interact with humans, which presumes that it actively searches for humans in order to interact with them. This paper addresses the problem how to plan robot's actions in order to maximize the number of such interactions in the case human behavior is not known in advance. We formulate this problem as the exploration/exploitation problem and design several strategies for the robot. The main contribution of the paper than lies in evaluation and comparison of the designed strategies on two datasets. The evaluation shows interesting properties of the strategies, which are discussed

Pankkialan operatiiviset riskit tulevaisuudessa

Opinnäytetyön tarkoitus oli kartoittaa operatiivisten riskien tulevaisuuden näkymiä pankkitoiminnassa Suomessa. Opinnäytetyön tutkimuksellisena tavoitteena oli asiantuntijoiden avustuksella selvittää tulevaisuuden näkymiä riskimaailmassa. Kehittämistavoitteena oli tuottaa konkreettisia suuntaviivoja tulevaisuuden riskien näkökulmaan ja niiden hallinnointiin. Tietoperustassa tarkasteltiin finanssialaa pankkialan näkökulmasta. Riskin, riskienhallinnan ja operatiivisen riskin määritelmiä sekä tulevaisuuden ennakointia. Tutkimuksellisessa osiossa tarkoituksena oli tuottaa tutkittavasta aiheesta sellaista tietoa ja ymmärrystä, jota voidaan hyödyntää tulevaisuudessa operatiivisten riskien tunnistamisessa ja hallinnassa. Pääaineiston keruu toteutettiin Delfoi-menetelmällä antamalla viisi tulevaisuuden väittämää panelistien arvioitavaksi. Tutkimustulokset osoittivat, että tulevaisuuden ennakoinnin ja riskienhallinnan yhdistäminen on edelleen haastavaa. Digitalisaatio tuo omat haasteet riskienhallinnalle, henkilöstön erikoisosaaminen nousee kilpailuvaltiksi, vastuullisuuteen kiinnitetään entistä enemmän huomiota asiakkaiden näkökulmasta sekä pankkitoiminta koetaan edelleen ihmisten väliseksi luottamuskaupaksi eikä sitä suhdetta voi digitalisoida. Johtopäätöksenä todetaan, että esille nousee selkeästi neljä teemaa, joihin tulee riskienhallinnan tulevaisuuden näkökulmasta reagoida. Nämä ovat digitalisaatio ja palveluiden siirtyminen verkkoon tuo haasteita verkkoturvallisuuden kannalta, yhteistyön vahvistaminen eri toimijoiden välillä noussee kilpailuvaltiksi, riskienhallinnan fokusointi vaatii robotiikan ja osaavan henkilöstön yhdistelmää sekä vastuullinen yritystoiminta vahvistuu. Opinnäytetyön tuotoksena tunnistettiin neljä mahdollista tulevaisuuden riskiä ja niiden hallintakeinot. Tutkimus antaa kokonaiskuvan tulevaisuuden näkökulmista operatiivisten riskien tunnistamisessa ja hallinnoinnissa. Opinnäytetyö on hyödynnettävissä yleisesti organisaatioissa riskienhallinnan kehittämisen tukena.The purpose of this study is to examine the future of the banking industry in Finland from the perspective of operational risks. The aim of this research is to examine with the help of experts the future views in the risk industry and to create a better insight of how the future looks and how operational risks can be managed. The theory section focuses on the financial industry from the perspective of banking, future forecasts and the definition of risk, risk management and operational risk. In the research section the aim was to produce information about the topic that can be used for identification and manage-ment of operational risks in the future. The main research data acquisition was conducted using the Delfoi-method by providing five future propositions to be evaluated by chosen panelists. The results of the study showed that combining the future perspective and risk management is still challenging. Digitalization brings its own challenges to risk management, special expertise of the employees will become a competitive asset, customers will pay increasing attention to sustainability and banking will still be considered as a confidential relationship between people, which therefore cannot be digitalized. The conclusion was that there are four clear themes that must be responded from the future perspective of risk management. These themes are digitalization and services moving online, which brings challenges from the perspective of online security, open cooperation and sharing of information will become a competitive asset, focusing of risk management will require a combination of robotics and humans, and sustainable entrepreneurship will become stronger. As an outcome of this study, a concept of possible future operational risks was gained, as well as risk management methods. The study provides an overview into the perspectives of the future of operational risk identification and management, and is also publicly available to give support to be utilized in the development of risk management in organizations

Assessment and management of iatrogenic withdrawal syndrome and delirium in pediatric intensive care units across Europe: an ESPNIC survey.

Analgesia and sedation are essential for the care of children in the pediatric intensive care unit (PICU); however, when prolonged, they may be associated with iatrogenic withdrawal syndrome (IWS) and delirium. We sought to evaluate current practices on IWS and delirium assessment and management (including non-pharmacologic strategies as early mobilization), and to investigate associations between presence of an analgosedation protocol and IWS and delirium monitoring, analgosedation weaning, and early mobilization. A multicenter cross-sectional survey-based study collecting data from one experienced physician or nurse per PICU in Europe was conducted from January to April 2021. We then investigated differences among PICUs that did or did not follow an analgosedation protocol. Among 357 PICUs, 215 (60%) responded across 27 countries. IWS was systematically monitored with a validated scale in 62% of PICUs, mostly using the Withdrawal Assessment Tool-1 (53%). Main first-line treatment for IWS was a rescue bolus with interruption of weaning (41%). Delirium was systematically monitored in 58% of PICUs, mostly with the Cornell Assessment of Pediatric Delirium scale (48%) and the Sophia Observation Scale for Pediatric Delirium (34%). Main reported first-line treatment for delirium was dexmedetomidine (45%) or antipsychotic drugs (40%). Seventy-one percent of PICUs reported to follow an analgosedation protocol. Multivariate analyses adjusted for PICU characteristics showed that PICUs using a protocol were significantly more likely to systematically monitor IWS (Odds Ratio [OR ]1.92, 95% Confidence Interval [CI] 1.01-3.67) and delirium (OR 2.00, 95% CI 1.07-3.72), use a protocol for analgosedation weaning (OR 6.38, 95% CI 3.20-12.71), and promote mobilization (OR 3.38, 95% CI 1.63-7.03). Monitoring and management of IWS and delirium are highly variable among European PICUs. The use of an analgosedation protocol was associated with increased likelihood of monitoring IWS and delirium, performing a structured analgosedation weaning, and promoting mobilization. Education on this topic and interprofessional collaborations are highly needed to help reduce the burden of analgosedation-associated adverse outcomes