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"This thesis presents learning and interaction algorithms to support a human teaching hierarchical task models to a robot using a single or multiple examples in the context of a mixed-initiative interaction with bi-directional communication. Our first contribution is an approach for learning a high level task from a single example using the bottom-up style. In particular, we have identified and implemented two important heuristics for suggesting task groupings and repetitions based on the data flow between tasks and on the physical structure of the manipulated artifact. We have evaluated our heuristics with users in a simulated environment and shown that the suggestions significantly improve the learning and interaction. For our second contribution, we extended this interaction by enabling users to teaching tasks using the top-down teaching style in addition to the bottom-up teaching style. Results obtained in a pilot study show that users utilize both the bottom-up and the top-down teaching styles to teach tasks. Our third contribution is an algorithm that merges multiple examples when there are alternative ways of doing a task. The merging algorithm is still under evaluation. "

Traditionally robots have been preprogrammed to execute specific tasks. Thisapproach works well in industrial settings where robots have to execute highlyaccurate movements, such as when welding. However, preprogramming a robot isalso expensive, error prone and time consuming due to the fact that every featuresof the task has to be considered. In some cases, where a robot has to executecomplex tasks such as playing the ball-in-a-cup game, preprogramming it mighteven be impossible due to unknown features of the task. With all this in mind,this thesis examines the possibility of combining a modern learning framework,known as Learning from Demonstrations (LfD), to first teach a robot how toplay the ball-in-a-cup game by demonstrating the movement for the robot, andthen have the robot to improve this skill by itself with subsequent ReinforcementLearning (RL). The skill the robot has to learn is demonstrated with kinestheticteaching, modelled as a dynamic movement primitive, and subsequently improvedwith the RL algorithm Policy Learning by Weighted Exploration with the Returns.Experiments performed on the industrial robot KUKA LWR4+ showed that robotsare capable of successfully learning a complex skill such as playing the ball-in-a-cupgame. / Traditionellt sett har robotar blivit förprogrammerade för att utföra specifika uppgifter.Detta tillvägagångssätt fungerar bra i industriella miljöer var robotar måsteutföra mycket noggranna rörelser, som att svetsa. Förprogrammering av robotar ärdock dyrt, felbenäget och tidskrävande eftersom varje aspekt av uppgiften måstebeaktas. Dessa nackdelar kan till och med göra det omöjligt att förprogrammeraen robot att utföra komplexa uppgifter som att spela bollen-i-koppen spelet. Medallt detta i åtanke undersöker den här avhandlingen möjligheten att kombinera ettmodernt ramverktyg, kallat inläraning av demonstrationer, för att lära en robothur bollen-i-koppen-spelet ska spelas genom att demonstrera uppgiften för denoch sedan ha roboten att själv förbättra sin inlärda uppgift genom att användaförstärkande inlärning. Uppgiften som roboten måste lära sig är demonstreradmed kinestetisk undervisning, modellerad som dynamiska rörelseprimitiver, ochsenare förbättrad med den förstärkande inlärningsalgoritmen Policy Learning byWeighted Exploration with the Returns. Experiment utförda på den industriellaKUKA LWR4+ roboten visade att robotar är kapabla att framgångsrikt lära sigspela bollen-i-koppen spelet

Traditional methods for Learning from Demonstration require users to train the robot through the entire process, or to provide feedback throughout a given task. These previous methods have proved to be successful in a selection of robotic domains; however, many are limited by the ability of the user to effectively demonstrate the task. In many cases, noisy demonstrations or a failure to understand the underlying model prevent these methods from working with a wider range of non-expert users. My insight is that in many mobile pick-and-place domains, teaching is done at a too fine grained level. In many such tasks, users are solely concerned with the end goal. This implies that the complexity and time associated with training and teaching robots through the entirety of the task is unnecessary. The robotic agent needs to know (1) a probable search location to retrieve the task's objects and (2) how to arrange the items to complete the task. This thesis work develops new techniques for obtaining such data from high-level spatial and temporal observations and demonstrations which can later be applied in new, unseen environments. This thesis makes the following contributions: (1) This work is built on a crowd robotics platform and, as such, we contribute the development of efficient data streaming techniques to further these capabilities. By doing so, users can more easily interact with robots on a number of platforms. (2) The presentation of new algorithms that can learn pick-and-place tasks from a large corpus of goal templates. My work contributes algorithms that produce a metric which ranks the appropriate frame of reference for each item based solely on spatial demonstrations. (3) An algorithm which can enhance the above templates with ordering constraints using coarse and noisy temporal information. Such a method eliminates the need for a user to explicitly specify such constraints and searches for an optimal ordering and placement of items. (4) A novel algorithm which is able to learn probable search locations of objects based solely on sparsely made temporal observations. For this, we introduce persistence models of objects customized to a user's environment.

Abstract This research explores the relationship between stories from the land and character education, and examines how the land forms the characters of those who live with Her. Twenty-seven participants were chosen through the snowball, or chain sampling, method from three groups of people living roughly within the boundaries of Hastings County, Ontario. Three groups of people were included: those living on Tyendinaga Mohawk Territory; generational farmers, whose families had settled in this area; and relative newcomers who have come to the area as recently as twenty years ago and self-identified as coming here in order to be closer to the land. Ages varied, with the youngest participant being 30 years old and the eldest being 94. Each participant was asked to share a story from his or her experience of living with the land in this area. During personal visits, stories were conveyed through conversations between the participant and the researcher, and recorded using both audio and video equipment. After transcription, the stories were coded for evidence of character development and reflected upon using three lenses: Noddings (2003) circles of caring; Haig-Brown’s (2010) ways through which the land teaches us; and the traditional Ojibway story of the Seven Grandfather Teachings. This reflection process was guided by the notion of close reading of the transcripts, close listening to audio recordings for voice inflection and laughter, and close watching of video recordings for body language. Findings from the research revealed rich connections between the stories and the three lenses used. Every aspect of character development was evidenced in one or more stories. The stories affirmed that, as the elders have traditionally taught, the land moulds people. Those who live in the same area develop character in similar ways. There was no substantial difference shown between the character traits evident in the stories given by Mohawk people and those from the other two groups. Character traits identified were reflective of the skills necessarily developed to live and work on this land. Implications for the development of character education curriculum emerged from the consideration of the use of locally based stories within classrooms. / Thesis (Ph.D, Education) -- Queen's University, 2011-12-17 11:03:25.805

We introduce a new methodological framework, called Bewextra, for the creation of the knowledge of needs in organizations. The development of our framework builds on theoretical engagement with literature from several disciplines including visioning and philosophy of needs as well as empirical data from vision development processes we have accompanied. To the best of our knowledge it is the first theoretical work that describes learning from an envisioned future and the generation of need knowledge as an abductive process in a methodologically replicable way. The advantages and practical implications of our method introduced are discussed in detail.

<p>Although there have been efforts to advance undergraduate chemistry laboratory learning, how graduate teaching assistants (GTAs) negotiate their teaching within-the-moment is still underexplored. This dissertation addresses this gap by foregrounding GTA experiences and pedagogies as foci of interest. The present study is divided into two phases. The first phase consisted of understanding the contextual meaning of eleven GTA participants’ self-recognized experiences via Communities of Practice and capital D Discourse analysis. The findings suggest that although participants recognize obligations to become better chemists as opposed to better teachers, they are active sensemakers of their pedagogies. However, due to obligations, the pedagogies they enact may inadvertently hinder learners’ sensemaking in their attempts to mitigate learners’ failures. Participants’ reliance on accuracy, completion, and efficiency within the laboratory led me to delve deeper into the theoretical conceptualizations of learning from successes and from failures. After creating the <i>Play First, Reflect Later </i>(<i>PFRL)</i> conceptual framework, I endeavored to better understand the extent that the chemistry laboratory can be integrated with productive failure. Thus, the second phase takes a more fine-grained approach in which nine participants were video recorded during their teaching and were later prompted to explain their rationale via video-stimulated recall interviews. Combining both the video and interview analysis conveys overlaps and incongruities. On one hand, participants effectively enact teaching practices that draws their learners’ attention to target concepts, leverage prior experience, and boosts affects. On the other, participants must not compromise learner agency and better prepare learners for long-term learning. Theoretically, errors and direct instruction should also be reconsidered for the laboratory context. I conclude by drawing implications for both researchers and practitioners. Namely, spaces in which GTAs learn to teach should be modified to be more learner-centric, collaborative, and inquiry based like the laboratories they are expected to teach. Furthermore, laboratory curricula (e.g., protocols and experiments) can be redesigned to facilitate learners to explore the hows and whys of their experiments with both their failures and successes. Changing the context of the chemistry laboratory itself, both in terms of teaching and curriculum, may be a more sustainable approach to enhance learners’ chemistry experiences. </p>

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