Robots exhibit flexible behavior largely in proportion to their degree of semantic knowledge about the world. Such knowledge is often meticulously hand-coded for a narrow class of tasks, limiting the scope of possible robot competencies. Thus, the primary limiting factor of robot capabilities is often not the physical attributes of the robot, but the limited time and skill of expert programmers. One way to deal with the vast number of situations and environments that robots face outside the laboratory is to provide users with simple methods for programming robots that do not require the skill of an expert. For this reason, learning from demonstration (LfD) has become a popular alternative to traditional robot programming methods, aiming to provide a natural mechanism for quickly teaching robots. By simply showing a robot how to perform a task, users can easily demonstrate new tasks as needed, without any special knowledge about the robot. Unfortunately, LfD often yields little semantic knowledge about the world, and thus lacks robust generalization capabilities, especially for complex, multi-step tasks. To address this shortcoming of LfD, we present a series of algorithms that draw from recent advances in Bayesian nonparametric statistics and control theory to automatically detect and leverage repeated structure at multiple levels of abstraction in demonstration data. The discovery of repeated structure provides critical insights into task invariants, features of importance, high-level task structure, and appropriate skills for the task. This culminates in the discovery of semantically meaningful skills that are flexible and reusable, providing robust generalization and transfer in complex, multi-step robotic tasks. These algorithms are tested and evaluated using a PR2 mobile manipulator, showing success on several complex real-world tasks, such as furniture assembly.

Yes / In March, the Department of Health (DH) released the Learning from Mistakes League, in which NHS organisations are ranked by levels of openness and transparency (DH, 2016). While a welcome first step toward the centralised and open promotion of learning since the publication of the Francis and Berwick reports three years earlier, unfortunately, the league can be considered misleading for a number of reasons.

The aim of this literature study is to find out how four Swedish scholars identify themeaning of the subject religious education in Sweden. Thereafter the result of theSwedish scholars interpretation is to be compared with British theories on how religiouseducation may be applied in schools through different strategies and principlesdeveloped by five different RE scholars.The method used in this thesis is a systematic literature study with a qualitative hermeneuticmethod. The empirical literature is analyzed and interpreted by me and thereafterplaced in the right context to read how the scholars view the subject. Accordingto the scholars and the Swedish curriculum, the results showed that the Swedishscholars have a similar view on what they think is the meaning with RE. The fundamentalidea is that the education should be child-centered and evolve around pupilsknowledge, background and abilities to attain the purpose of the subject. Which willlead to an immersed knowledge, understanding, respect and tolerance for different religiouspositions.Both the British and the Swedish scholars agreed that the dominating secular and atheistpoint of view in the classrooms and in the society, constitutes challenges for theteachers to apply the different strategies to achieve the meaning of RE. The Swedishscholars opine that the RE needs to contribute to learning about and learning fromreligions through different perspectives that show religions from the inside perspectiveand the outside perspective.

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Social network sites (SNSs) have become ubiquitous around the globe and interwoven with all aspects of life. In this article, I will argue that the communicative infrastructure of SNSs, i.e., all SNS-elements that allow users to communicate, is a key element for understanding their impact as it creates environments in which users, their behaviors, and social interactions are embedded. These digital environments facilitate and encourage fundamental mechanisms of implicit learning from feedback as well as observation in an unprecedented way. I will discuss how these technologybased learning environments impact the mental health of their users, e.g., by linking negative online feedback to depression and following influencers to disturbed eating. The article ends with a conclusion that emphasizes the advantages of understanding SNSs as environments in order to reflect the complexity, relevance, and ubiquitousness of the phenomenon.

The purpose of this report was to investigate how people who order a soil analysis perceive the information about that process. According to the law the customers need to have information about the soil analysis if they order it. A new informational film about soil analysis was compared to the existing data to see how the customer perceives different types of information. Two groups took part in the survey, one group watched the informational film, and the second group only had access to the existing data. The results of the investigation show that the majority of the respondents in the second group think that there is too little information about soil analysis and that it is hard to find. The first group think that after watching the movie, they got all the necessary information about the soil analysis in an accessible way. However, most survey respondents want the information in text form. Those who saw the informational movie think they have a better understanding of how and why the soil analysis is done, compared to those who only had access to the existing information. Most survey respondents want to get information from the municipality / county government, but receive the most of it from consultant agencies.

Robots are destined to move beyond the caged factory floors towards domains where they will be interacting closely with humans. They will encounter highly varied environments, scenarios and user demands. As a result, programming robots after deployment will be an important requirement. To address this challenge, the field of Learning from Demonstration (LfD) emerged with the vision of programming robots through demonstrations of the desired behavior instead of explicit programming. The field of LfD within robotics has been around for more than 30 years and is still an actively researched field. However, very little research is done on the implications of having a non-robotics expert as a teacher. This thesis aims to bridge this gap by developing learning from demonstration algorithms and interaction paradigms that allow non-expert people to teach robots new skills. The first step of the thesis was to evaluate how non-expert teachers provide demonstrations to robots. Keyframe demonstrations are introduced to the field of LfD to help people teach skills to robots and compared with the traditional trajectory demonstrations. The utility of keyframes are validated by a series of experiments with more than 80 participants. Based on the experiments, a hybrid of trajectory and keyframe demonstrations are proposed to take advantage of both and a method was developed to learn from trajectories, keyframes and hybrid demonstrations in a unified way. A key insight from these user experiments was that teachers are goal oriented. They concentrated on achieving the goal of the demonstrated skills rather than providing good quality demonstrations. Based on this observation, this thesis introduces a method that can learn actions and goals from the same set of demonstrations. The action models are used to execute the skill and goal models to monitor this execution. A user study with eight participants and two skills showed that successful goal models can be learned from non- expert teacher data even if the resulting action models are not as successful. Following these results, this thesis further develops a self-improvement algorithm that uses the goal monitoring output to improve the action models, without further user input. This approach is validated with an expert user and two skills. Finally, this thesis builds an interactive LfD system that incorporates both goal learning and self-improvement and evaluates it with 12 naive users and three skills. The results suggests that teacher feedback during experiments increases skill execution and monitoring success. Moreover, non-expert data can be used as a seed to self-improvement to fix unsuccessful action models.

Two projects are documented within this MEM Report: I. The first project examined what was learnt involving the critical infrastructure in the aftermath of natural disasters in the Canterbury region of New Zealand – the most prominent being the series of earthquakes between 2010 and 2011. The project identified several learning gaps, leading to recommendations for further investigations that could add significant value for the lifeline infrastructure community. II. Following the Lifeline Lesson Learnt Project, the Disaster Mitigation Guideline series was initiated with two booklets, one on Emergency Potable Water and a second on Emergency Sanitation. The key message from both projects is that we can and must learn from disasters. The projects described are part of the emergency management, and critical infrastructure learning cycles – presenting knowledge captured by others in a digestible format, enabling the lessons to be reapplied. Without these kinds of projects, there will be fewer opportunities to learn from other’s successes and failures when it comes to preparing for natural disasters.

"We address the problem of performing complex tasks for a robot operating in changing environments. We propose two approaches to the following problem: 1) define task-specific cost functions for motion planning that represent path quality by learning from an expert's preferences and 2) using constraint-based representation of the task inside learning from demonstration paradigm. In the first approach, we generate a set of paths for a given task using a motion planner and collect data about their features (path length, distance from obstacles, etc.). We provide these paths to an expert as a set of pairwise comparisons. We then form a ranking of the paths from the expert's comparisons. This ranking is used as training data for learning algorithms, which attempt to produce a cost function that maps path feature values to a cost that is consistent with the expert's ranking. We test our method on two simulated car-maintenance tasks with the PR2 robot: removing a tire and extracting an oil filter. We found that learning methods which produce non-linear combinations of the features are better able to capture expert preferences for the tasks than methods which produce linear combinations. This result suggests that the linear combinations used in previous work on this topic may be too simple to capture the preferences of experts for complex tasks. In the second approach, we propose to introduce a constraint-based description of the task that can be used together with the motion planner to produce the trajectories. The description is automatically created from the demonstration by performing segmentation and extracting constraints from the motion. The constraints are represented with the Task Space Regions (TSR) that are extracted from the demonstration and used to produce a desired motion. To account for the parts of the motion where constraints are different a segmentation of the demonstrated motion is performed using TSRs. The proposed approach allows performing tasks on robot from human demonstration in changing environments, where obstacle distribution or poses of the objects could change between demonstration and execution. The experimental evaluation on two example motions was performed to estimate the ability of our approach to produce the desired motion and recover a demonstrated trajectory."

Prior to 1989, in New Zealand, very few women were represented in senior leadership positions in schools, especially co-educational secondary schools. Following the 1989 Education Act, commonly referred to as the 'Tomorrow's Schools' legislation, women began to be appointed in increasing numbers to school principalships. In New Zealand, as in other western democracies where New Right ideologies have impacted on educational policy, the role of the school principal has become more demanding and complex. If principals are to be supported in this role, it is considered important that the knowledge and experiences which influence their professional practice are better understood so that professional development programmes are targeted appropriately. This study builds on previous studies of women's experiences of leadership in education (Neville, 1988; Shakeshaft, 1989, 1995; Strachan, 1991, 1997; Court, 1992; Ozga & Walker, 1995; Hall, 1996; Coleman, 1996, 2000; Ah Nee-Benham & Cooper, 1998; Henderson-Kelly & Pamphilon, 2000; McCarthy, 2001; McLay & Brown, 2001) but has as its focus, the sources of influence on five women principals' practice. It has a complementary focus to McLay's and Brown's (2001) study of women headteachers in UK independent secondary schools. They investigated the women's formal training but also sought to find out what life experiences might have prepared them for the role of leading a school. Ah Nee-Benham's and Cooper's (1998) narratives of minority women in school leadership positions in the United States provided the inspiration for this study of five New Zealand principals. While the women in this study are not 'voices from the margins' as are the women in Ah Nee-Benham's and Cooper's study, through the use of narrative and other qualitative methods, their individual accounts capture the voice of experience which is too often missing from the educational leadership literature (Ah Nee-Benham & Cooper, 1998; Heck & Hallinger, 1999; Southworth, 2001). The women's stories add collectively to an emerging literature base which seeks to unravel how leaders actually think and work. This study specifically examines five women principals' personal theories and beliefs about leadership and investigates how their personal histories and career experiences have influenced their professional knowledge and practice. The research methods reflect a commitment to understanding the women's experiences from their perspectives and their stories are seen as providing a window into what they know. Methods such as concept mapping, open-ended unstructured interviews and annals (snake diagrams) help unpack their personal theories about leadership as well as assist understanding of the influence of their personal histories and professional experiences. The educational leadership literature provides a useful starting point to analyse the first part of the research question regarding the women's conceptions of leadership. The literature acknowledges various approaches to leadership, three of which may be called 'moral/critical', 'people-centred' and 'corporate managerial'. Respectively, they each have 'world-views' which emphasise 'ideals', 'people' and 'efficiency'. In this study, I aim to identify what 'world-views' underpin the women's conceptions of leadership but also how they have constructed these concepts. The different needs of each of their schools in association with the principals' own backgrounds and values systems have strongly influenced their varying conceptions of leadership. While on one level their beliefs are unique, on another, they reveal some common themes. The principals are people-focussed but this commitment is underpinned by deeply held ideals about the purpose of schools enriching lives. Efficient management systems are seen as important supporting concepts to assisting people and accomplishing ideals. In order to answer the second part of the research question regarding how the women's personal histories and professional experiences have influenced their professional knowledge and practice, this study drew on the findings of an Australian study which investigated the sources of influence on teacher knowledge in action (McMeniman, Cumming, Wilson, Stevenson & Sim, 2000). It provided the platform and focus for the current study of women principals. A key finding of this study is that the women's beliefs and practices have been influenced by both their personal histories and various career experiences which have assisted them to grow professionally. Central to their learning from these experiences, however, is the way in which the individual interprets or filters experience and actively constructs meaning. Principals' knowledge bases are socially constructed but mediated by their personal theories. Learning from experiences in the workplace has a critical influence especially if it is scaffolded by experts (Billett, 2001) or if there is a community of practice (Wenger, 1998, 2000) encouraging participation. When this learning is augmented by various research access points (McMeniman et al., 2000) such as critical dialogue with peers, in-service education and formal postgraduate studies, it potentially has a direct and daily influence on principals' practice. Although this study is limited to an analysis of five women principals, the commonalities in the sources of influence on their practice, lead one to suggest that the findings may have relevance for other principals, both male and female, and possibly other practitioners.