Explainable and Interpretable Decision-Making for Robotic Tasks

Future generations of robots, such as service robots that support humans with household tasks, will be a pervasive part of our daily lives. The human\u27s ability to understand the decision-making process of robots is thereby considered to be crucial for establishing trust-based and efficient interactions between humans and robots. In this thesis, we present several interpretable and explainable decision-making methods that aim to improve the human\u27s understanding of a robot\u27s actions, with a particular focus on the explanation of why robot failures were committed.In this thesis, we consider different types of failures, such as task recognition errors and task execution failures. Our first goal is an interpretable approach to learning from human demonstrations (LfD), which is essential for robots to learn new tasks without the time-consuming trial-and-error learning process. Our proposed method deals with the challenge of transferring human demonstrations to robots by an automated generation of symbolic planning operators based on interpretable decision trees. Our second goal is the prediction, explanation, and prevention of robot task execution failures based on causal models of the environment. Our contribution towards the second goal is a causal-based method that finds contrastive explanations for robot execution failures, which enables robots to predict, explain and prevent even timely shifted action failures (e.g., the current action was successful but will negatively affect the success of future actions). Since learning causal models is data-intensive, our final goal is to improve the data efficiency by utilizing prior experience. This investigation aims to help robots learn causal models faster, enabling them to provide failure explanations at the cost of fewer action execution experiments.In the future, we will work on scaling up the presented methods to generalize to more complex, human-centered applications

Why did I fail? A Causal-based Method to Find Explanations for Robot Failures

Robot failures in human-centered environments are inevitable. Therefore, the ability of robots to explain such failures is paramount for interacting with humans to increase trust and transparency. To achieve this skill, the main challenges addressed in this paper are I) acquiring enough data to learn a cause-effect model of the environment and II) generating causal explanations based on that model. We address I) by learning a causal Bayesian network from simulation data. Concerning II), we propose a novel method that enables robots to generate contrastive explanations upon task failures. The explanation is based on setting the failure state in contrast with the closest state that would have allowed for successful execution, which is found through breadth-first search and is based on success predictions from the learned causal model. We assess the sim2real transferability of the causal model on a cube stacking scenario. Based on real-world experiments with two differently embodied robots, we achieve a sim2real accuracy of 70% without any adaptation or retraining. Our method thus allowed real robots to give failure explanations like, 'the upper cube was dropped too high and too far to the right of the lower cube.'Comment: submitted to IEEE Robotics and Automation Letters (February 2022

Why Did I Fail? a Causal-Based Method to Find Explanations for Robot Failures

Robot failures in human-centered environments are inevitable. Therefore, the ability of robots to explain such failures is paramount for interacting with humans to increase trust and transparency. To achieve this skill, the main challenges addressed in this paper are I) acquiring enough data to learn a cause-effect model of the environment and II) generating causal explanations based on the obtained model. We address I) by learning a causal Bayesian network from simulation data. Concerning II), we propose a novel method that enables robots to generate contrastive explanations upon task failures. The explanation is based on setting the failure state in contrast with the closest state that would have allowed for successful execution. This state is found through breadth-first search and is based on success predictions from the learned causal model. We assessed our method in two different scenarios I) stacking cubes and II) dropping spheres into a container. The obtained causal models reach a sim2real accuracy of 70% and 72%, respectively. We finally show that our novel method scales over multiple tasks and allows real robots to give failure explanations like “the upper cube was stacked too high and too far to the right of the lower cube.

A causal-based approach to explain, predict and prevent failures in robotic tasks

Robots working in human environments need to adapt to unexpected changes to avoid failures. This is an open and complex challenge that requires robots to timely predict and identify the causes of failures in order to prevent them. In this paper, we present a causal-based method that will enable robots to predict when errors are likely to occur and prevent them from happening by executing a corrective action. Our proposed method is able to predict immediate failures and also failures that will occur in the future. The latter type of failure is very challenging, and we call them timely-shifted action failures (e.g., the current action was successful but will negatively affect the success of future actions). First, our method detects the cause–effect relationships between task executions and their consequences by learning a causal Bayesian network (BN). The obtained model is transferred from simulated data to real scenarios to demonstrate the robustness and generalization of the obtained models. Based on the causal BN, the robot can predict if and why the executed action will succeed or not in its current state. Then, we introduce a novel method that finds the closest success state through a contrastive Breadth-First-Search if the current action was predicted to fail. We evaluate our approach for the problem of stacking cubes in two cases; (a) single stacks (stacking one cube) and; (b) multiple stacks (stacking three cubes). In the single-stack case, our method was able to reduce the error rate by 97%. We also show that our approach can scale to capture various actions in one model, allowing us to measure the impact of an imprecise stack of the first cube on the stacking success of the third cube. For these complex situations, our model was able to prevent around 95% of the stacking errors. Thus, demonstrating that our method is able to explain, predict, and prevent execution failures, which even scales to complex scenarios that require an understanding of how the action history impacts future actions

From "guest workers" to EU migrants: A gendered view on the labour market integration of different arrival cohorts in Germany

Objective: This paper draws on data from the Microcensus to provide a long-term overview of the labour market performance of different arrival cohorts of non-German women and men who immigrated to (western) Germany. Background: While there is a large body of research on the labour market outcomes of migrants to Germany, a long-term and gender-specific overview is missing. Method: We provide descriptive analyses of the employment rates, working hours, and occupational status levels of different arrival cohorts by gender, calendar year, and duration of stay. The data cover the time period 1976-2015. Results: With the exception of the earliest cohort, migrant women and men were consistently less likely to be employed than their German counterparts. While the average working hours of migrant women of earlier cohorts were longer than those of German women, this pattern reversed due to a considerable decline in the average working hours of migrant women across subsequent cohorts. The occupational status levels of female and male migrants increased across the arrival cohorts, corresponding to higher levels of education. Analyses by duration of stay indicate that the occupational status of the arrival cohorts tended to decline during their initial years of residence, and to stagnate thereafter. This pattern seems to be due in part to selective outmigration. Conclusion: Our results clearly show that the labour market performance of immigrants varied greatly by arrival cohort, reflecting the conditions and policy contexts during which they entered Germany. This conclusion applied especially to migrant women

Спонсорство и благотворительность как составная часть корпоративной социальной ответственности (на примере группы ВТБ)

Актуальность работы обусловлена повышенным интересом к вопросам реализации корпоративной социальной политики в контексте мирового экономического кризиса 2008-2010 гг. Цель работы - провести анализ форм реализации корпоративной социальной ответственности в российской финансовой сфере на примере группы ВТБ. Выводы, представленные в работе, получены с использованием таких методов, как анализ и синтез, дедукция, графические методы. Основные результаты работы включают в себя сравнительную характеристику основных форм спонсорской и благотворительной помощи группы ВТБ по базовым направлениям и субъектам за 2011-2012 гг. согласно данным официальных отчетов по корпоративной социальной ответственности, размещенных на официальном сайте организации. Также выявлены достоинства и недостатки корпоративной социальной политики группы ВТБ. В работе доказано, что корпоративная социальная политика группы ВТБ носит диспропорциональный характер, что отражает приоритеты компании в работе с клиентами и местным сообществом

Search for dark matter produced in association with bottom or top quarks in √s = 13 TeV pp collisions with the ATLAS detector

A search for weakly interacting massive particle dark matter produced in association with bottom or top quarks is presented. Final states containing third-generation quarks and miss- ing transverse momentum are considered. The analysis uses 36.1 fb−1 of proton–proton collision data recorded by the ATLAS experiment at √s = 13 TeV in 2015 and 2016. No significant excess of events above the estimated backgrounds is observed. The results are in- terpreted in the framework of simplified models of spin-0 dark-matter mediators. For colour- neutral spin-0 mediators produced in association with top quarks and decaying into a pair of dark-matter particles, mediator masses below 50 GeV are excluded assuming a dark-matter candidate mass of 1 GeV and unitary couplings. For scalar and pseudoscalar mediators produced in association with bottom quarks, the search sets limits on the production cross- section of 300 times the predicted rate for mediators with masses between 10 and 50 GeV and assuming a dark-matter mass of 1 GeV and unitary coupling. Constraints on colour- charged scalar simplified models are also presented. Assuming a dark-matter particle mass of 35 GeV, mediator particles with mass below 1.1 TeV are excluded for couplings yielding a dark-matter relic density consistent with measurements

COVID-19 symptoms at hospital admission vary with age and sex: results from the ISARIC prospective multinational observational study

Background: The ISARIC prospective multinational observational study is the largest cohort of hospitalized patients with COVID-19. We present relationships of age, sex, and nationality to presenting symptoms. Methods: International, prospective observational study of 60 109 hospitalized symptomatic patients with laboratory-confirmed COVID-19 recruited from 43 countries between 30 January and 3 August 2020. Logistic regression was performed to evaluate relationships of age and sex to published COVID-19 case definitions and the most commonly reported symptoms. Results: ‘Typical’ symptoms of fever (69%), cough (68%) and shortness of breath (66%) were the most commonly reported. 92% of patients experienced at least one of these. Prevalence of typical symptoms was greatest in 30- to 60-year-olds (respectively 80, 79, 69%; at least one 95%). They were reported less frequently in children (≤ 18 years: 69, 48, 23; 85%), older adults (≥ 70 years: 61, 62, 65; 90%), and women (66, 66, 64; 90%; vs. men 71, 70, 67; 93%, each P < 0.001). The most common atypical presentations under 60 years of age were nausea and vomiting and abdominal pain, and over 60 years was confusion. Regression models showed significant differences in symptoms with sex, age and country. Interpretation: This international collaboration has allowed us to report reliable symptom data from the largest cohort of patients admitted to hospital with COVID-19. Adults over 60 and children admitted to hospital with COVID-19 are less likely to present with typical symptoms. Nausea and vomiting are common atypical presentations under 30 years. Confusion is a frequent atypical presentation of COVID-19 in adults over 60 years. Women are less likely to experience typical symptoms than men