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Dissertations / Theses on the topic 'Demonstration Learning'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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1

Suay, Halit Bener. "Reinforcement Learning from Demonstration." Digital WPI, 2016. https://digitalcommons.wpi.edu/etd-dissertations/173.

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Off-the-shelf Reinforcement Learning (RL) algorithms suffer from slow learning performance, partly because they are expected to learn a task from scratch merely through an agent's own experience. In this thesis, we show that learning from scratch is a limiting factor for the learning performance, and that when prior knowledge is available RL agents can learn a task faster. We evaluate relevant previous work and our own algorithms in various experiments. Our first contribution is the first implementation and evaluation of an existing interactive RL algorithm in a real-world domain with a human

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Billing, Erik. "Cognition Reversed : Robot Learning from Demonstration." Licentiate thesis, Umeå universitet, Institutionen för datavetenskap, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-14407.

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The work presented in this thesis investigates techniques for learning from demonstration (LFD). LFD is a well established approach to robot learning, where a teacher demonstrates a behavior to a robot pupil. This thesis focuses on LFD where a human teacher demonstrates a behavior by controlling the robot via teleoperation. The robot should after demonstration be able to execute the demonstrated behavior under varying conditions. Several views on representation, recognition and learning of robot behavior are presented and discussed from a cognitive and computational perspective. LFD-related co

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Ekvall, Staffan. "Robot Task Learning from Human Demonstration." Doctoral thesis, Stockholm : School of Computer Science and Communication, Kungliga Tekniska högskolan (KTH), 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-4279.

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4

Junges, Robert, and Franziska Klügl. "Learning Agent Models in SeSAm : (Demonstration)." Örebro universitet, Institutionen för naturvetenskap och teknik, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-29234.

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Designing the agent model in a multiagent simulation is a challenging task due to the generative nature of such systems. In this contribution we present an extension to the multiagent simulation platform SeSAm, introducing a learning-based design strategy for building agent behavior models.

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5

Michini, Bernard (Bernard J. ). "Bayesian nonparametric reward learning from demonstration." Thesis, Massachusetts Institute of Technology, 2013. http://hdl.handle.net/1721.1/85762.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2013.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 123-132).<br>Learning from demonstration provides an attractive solution to the problem of teaching autonomous systems how to perform complex tasks. Demonstration opens autonomy development to non-experts and is an intuitive means of communication for humans, who naturally use demonstration to teach others. This thesis focuses on a specific form of learning from demonstration, namely inverse reinforcement

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Pillai, Sudeep. "Learning articulated motions from visual demonstration." Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/89861.

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Thesis: S.M. in Computer Science and Engineering, Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2014.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>35<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 94-98).<br>Robots operating autonomously in household environments must be capable of interacting with articulated objects on a daily basis. They should be able to infer each object's u

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Gutierrez, Reymundo A. (Reymundo Alejandro). "Learning task-oriented grasp heuristics from demonstration." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/113153.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 79-82).<br>When people plan their motions for dexterous work, they implicitly consider the next likely step in the action sequence. Almost without conscious thought, we select a grasp that meets the implicit constraints rela

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8

Lau, Tessa. "Programming by demonstration : a machine learning approach /." Thesis, Connect to this title online; UW restricted, 2001. http://hdl.handle.net/1773/6949.

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9

Olenderski, Adam P. "Aspects of behavior design for learning by demonstration." abstract and full text PDF (free order & download UNR users only), 2007. http://0-gateway.proquest.com.innopac.library.unr.edu/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:1446440.

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10

Rozo, Leonel. "Robot learning from demonstration of force-based manipulation tasks." Doctoral thesis, Universitat Politècnica de Catalunya, 2013. http://hdl.handle.net/10803/129566.

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One of the main challenges in Robotics is to develop robots that can interact with humans in a natural way, sharing the same dynamic and unstructured environments. Such an interaction may be aimed at assisting, helping or collaborating with a human user. To achieve this, the robot must be endowed with a cognitive system that allows it not only to learn new skills from its human partner, but also to refine or improve those already learned. In this context, learning from demonstration appears as a natural and userfriendly way to transfer knowledge from humans to robots. This dissertation addre

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Korkinof, Dimitrios. "Probabilistic learning by demonstration from complete and incomplete data." Thesis, Imperial College London, 2015. http://hdl.handle.net/10044/1/24799.

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In recent years we have observed a convergence of the fields of robotics and machine learning initiated by technological advances bringing AI closer to the physical world. A prerequisite, however, for successful applications is to formulate reliable and precise offline algorithms, requiring minimal tuning, fast and adaptive online algorithms and finally effective ways of rectifying corrupt demonstrations. In this work we aim to address some of those challenges. We begin by employing two offline algorithms for the purpose of Learning by Demonstration (LbD). A Bayesian non-parametric approach, a

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12

Yadav, Mayank. "Learning Robotic Reactive Behaviour from Demonstration via Dynamic Tree." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-285563.

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Programming a complex robot is difficult, time-consuming and expensive. Learning from Demonstration (LfD) is a methodology where a teacher demonst--rates a task and the robot learns to execute the task. This thesis presents a method which generates reactive robot behaviour learned from demonstration where sequences of action are implicitly coded in a rule-based manner. It also presents a novel approach to find behaviour hierarchy among behaviours of a demonstration.In the thesis, the system learns the activation rule of primitives as well as the association that should be performed between sen

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13

dePalma, Nicholas Brian. "Task transparency in learning by demonstration : gaze, pointing, and dialog." Thesis, Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/34702.

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This body of work explores an emerging aspect of human-robot interaction, transparency. Socially guided machine learning has proven that highly immersive robotic behaviors have yielded better results than lesser interactive behaviors for performance and shorter training time. While other work explores this transparency in learning by demonstration using non-verbal cues to point out the importance or preference users may have towards behaviors, my work follows this argument and attempts to extend it by offering cues to the internal task representation. What I show is that task-transparency, o

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14

Mohseni-Kabir, Anahita. "Collaborative Learning of Hierarchical Task Networks from Demonstration and Instruction." Digital WPI, 2015. https://digitalcommons.wpi.edu/etd-theses/1033.

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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 h

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Nguyen, Duc-Canh. "Learning socio-communicative behaviors of a humanoid robot by demonstration." Thesis, Université Grenoble Alpes (ComUE), 2018. http://www.theses.fr/2018GREAT076/document.

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Un robot d'assistance sociale (SAR) est destiné à engager les gens dans une interaction située comme la surveillance de l'exercice physique, la réadaptation neuropsychologique ou l'entraînement cognitif. Alors que les comportements interactifs de ces systèmes sont généralement scriptés, nous discutons ici du cadre d’apprentissage de comportements interactifs multimodaux qui est proposé par le projet SOMBRERO.Dans notre travail, nous avons utilisé l'apprentissage par démonstration afin de fournir au robot des compétences nécessaires pour effectuer des tâches collaboratives avec des partenaires

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Blackburn, Michael. "Site design : Indiana Dunes environmental learning center sustainable systems demonstration area." Virtual Press, 2001. http://liblink.bsu.edu/uhtbin/catkey/1244867.

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This creative project explores the principles of permaculture, within the context of environmental sustainability, applying them to the development of the master plan for Camp Goodfellow. Camp Good Fellow is the also known as the Indiana Dunes Environmental Learning Center, part of the Indiana Dunes National Lakeshore located in northwestern Indiana.This project involves design of an overnight environmental education facility within the Goodfellow site. Permaculture principles are applied to the development of a third camp cluster at Goodfellow, focused on sustainable systems demonstration. Th

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17

Narayanan, Krishna Kumar [Verfasser]. "Learning Vision based Mobile Robot Behaviors from Demonstration / Krishna Kumar Narayanan." München : Verlag Dr. Hut, 2016. http://d-nb.info/1084386291/34.

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18

Donda, Krupali. "Low-frequency Absorbing Acoustic Metasurfaces : Deep-learning Approach and Experimental Demonstration." Electronic Thesis or Diss., Université de Lorraine, 2021. http://www.theses.fr/2021LORR0268.

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L'avènement et le développement des métamatériaux/métasurfaces acoustiques au cours des dernières années ont bouleversé les approches conventionnels concernant de la propagation et de la manipulation des ondes acoustiques et même au-delà. Dans le contexte de l'absorption acoustique, ces structures artificielles ont ouvert des possibilités et des opportunités sans précédent pour s’atteler aux problèmes de l’absorption acoustique et du bruit en très basses fréquences défiant les limitations physiques classiques. L'objectif principal de cette thèse est de concevoir et réaliser des métasurfaces ac

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19

Skoglund, Alexander. "Programming by demonstration of robot manipulators." Doctoral thesis, Örebro : Örebro University, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-6570.

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Fernandes, Martins Murilo. "A framework for learning by demonstration in multi-teacher multi-robot scenarios." Thesis, Imperial College London, 2012. http://hdl.handle.net/10044/1/9304.

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As robots become more accessible to humans, more intuitive and human-friendly ways of programming them with interactive and group-aware behaviours are needed. This thesis addresses the gap between Learning by Demonstration and Multi-robot systems. In particular, this thesis tackles the fundamental problem of learning multi-robot cooperative behaviour from concurrent multi-teacher demonstrations, problem which had not been addressed prior to this work. The core contribution of this thesis is the design and implementation of a novel, multi- layered framework for multi-robot learning from simulta

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21

Akgun, Baris. "Robots learning actions and goals from everyday people." Diss., Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/54435.

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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

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22

Andrews, David B. "The half-silvered mirror : brain assessment of learning and learning skills improvement; a demonstration project with 8th graders /." The Ohio State University, 1986. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487266691094649.

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23

Michieletto, Stefano. "Robot Learning by observing human actions." Doctoral thesis, Università degli studi di Padova, 2014. http://hdl.handle.net/11577/3423766.

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Nowadays, robotics is entering in our life. One can see robot in industries, offices and even in homes. The more robots are in contact with people, the more requests of new capabilities and new features increase, in order to make robots able to act in case of need, help humans or be a companion. Therefore, it becomes essential to have a quick and easy way to teach new skills to robots. That is the aim of Robot Learning from Demonstration. This paradigm allows to directly program new tasks in a robot through demonstrations. This thesis proposes a novel approach to Robot Learning from Demonst

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Carnegie, Evelyn Mary. "The effectiveness of demonstration in conveying kinematic and kinetic information for skill acquisition." Thesis, University of Ulster, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.338800.

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25

Gritsenko, Artem. "Learning From Demonstrations in Changing Environments: Learning Cost Functions and Constraints for Motion Planning." Digital WPI, 2015. https://digitalcommons.wpi.edu/etd-theses/1246.

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"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

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Soh, Harold Soon Hong. "Online spatio-temporal learning and prediction for adaptive robotic systems : applications in tactile classification and learning assistance by demonstration." Thesis, Imperial College London, 2013. http://hdl.handle.net/10044/1/17836.

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Successful biological systems adapt to change. Humans, for example, are capable of continual self-improvement and gain new skills with experience. Similar online learning characteristics would enable robotic systems to autonomously improve their capabilities over time. In this thesis, we focus on the problem of iteratively learning from multivariate time-series; the "raw material" that we use to make inferences about the future. We adopt a combined approach: gaining inspiration from biological systems, in particular recurrent neural networks, and merging these ideas with recent advances in sta

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Toris, Russell C. "Spatial and Temporal Learning in Robotic Pick-and-Place Domains via Demonstrations and Observations." Digital WPI, 2016. https://digitalcommons.wpi.edu/etd-dissertations/135.

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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.

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Avverahalli, Ravi Darshan. "Identifying and Prioritizing Critical Information in Military IoT: Video Game Demonstration." Thesis, Virginia Tech, 2021. http://hdl.handle.net/10919/104070.

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Current communication and network systems are not built for delay-sensitive applications. The most obvious fact is that the communication capacity is only achievable in theory with infinitely long codes, which means infinitely long delays. One remedy for this is to use shorter codes. Conceptually, there is a deeper reason for the difficulties in such solutions: in Shannon's original 1948 paper, he started out by stating that the "semantic aspects" of information is "irrelevant" to communications. Hence, in Shannon's communication system, as well as every network built after him, we put all inf

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Fonooni, Benjamin. "Cognitive Interactive Robot Learning." Doctoral thesis, Umeå universitet, Institutionen för datavetenskap, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-97422.

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Building general purpose autonomous robots that suit a wide range of user-specified applications, requires a leap from today's task-specific machines to more flexible and general ones. To achieve this goal, one should move from traditional preprogrammed robots to learning robots that easily can acquire new skills. Learning from Demonstration (LfD) and Imitation Learning (IL), in which the robot learns by observing a human or robot tutor, are among the most popular learning techniques. Showing the robot how to perform a task is often more natural and intuitive than figuring out how to modify a

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DUAN, ANQING. "A Structured Prediction Approach to Robot Imitation Learning." Doctoral thesis, Università degli studi di Genova, 2021. http://hdl.handle.net/11567/1050082.

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This thesis is primarily focused on movement primitives-based imitation learn- ing, within the context of robot programming by demonstration. Specifically, the imitation problem is tackled from a supervised-learning perspective. Therefore, it allows us to resort to theoretical tools from structured prediction, which can handle data-sets with complex structures. The first part of the thesis provides an overall background, in which we overview state-of-the-art imitation learning algorithms as well as discuss relevant technical tools. We formally introduce our contribution in part II. Our

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Lin, Hsiu-Chin. "Novel approach for representing, generalising, and quantifying periodic gaits." Thesis, University of Edinburgh, 2015. http://hdl.handle.net/1842/14180.

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Our goal is to introduce a novel method for representing, generalising, and comparing gaits; particularly, walking gait. Human walking gaits are a result of complex, interdependent factors that include variations resulting from embodiments, environment and tasks, making techniques that use average template frameworks suboptimal for systematic analysis or corrective interventions. The proposed work aims to devise methodologies for being able to represent gaits and gait transitions such that optimal policies that eliminate the inter-personal variations from tasks and embodiment may be recovered.

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Abbas, Ahmed Khodair Abbas. "A dynamic planner for object assembly tasks based learning the spatial relationships of its parts from a single demonstration." Thesis, Queensland University of Technology, 2021. https://eprints.qut.edu.au/207935/1/Ahmed%20Khodair%20Abbas_Abbas_Thesis.pdf.

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The main application of collaborative robots is helping workers during assembly tasks. Most assembly task robots are limited to fixed scenarios that do not require reasoning. To address the current limitations, this thesis introduces a goal-directed dynamic AI system that enables robots to help workers during assembly tasks. After observing an object being assembled, our AI system can induce a 3D model of the assembled object. This induced model encodes the spatial relationships between the components of the object and allows a dynamic planner to repair assembly plans when the worker

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Tunaoglu, Doruk. "Implementation Of A Closed-loop Action Generation System On A Humanoid Robot Through Learning By Demonstration." Master's thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/12612477/index.pdf.

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In this thesis the action learning and generation problem on a humanoid robot is studied. Our aim is to realize action learning, generation and recognition in one system and our inspiration source is the mirror neuron hypothesis which suggests that action learning, generation and recognition share the same neural circuitry. Dynamic Movement Primitives, an efficient action learning and generation approach, are modified in order to fulfill this aim. The system we developed (1) can learn from multiple demonstrations, (2) can generalize to different conditions, (3) generates actions in a closed-lo

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Cheng, Gang [Verfasser], and Jianwei [Akademischer Betreuer] Zhang. "State-Action Gist based In-hand Manipulation Learning from Human Demonstration / Gang Cheng. Betreuer: Jianwei Zhang." Hamburg : Staats- und Universitätsbibliothek Hamburg, 2013. http://d-nb.info/1038789826/34.

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Šošić, Adrian [Verfasser], Abdelhak M. [Akademischer Betreuer] Zoubir, and Heinz [Akademischer Betreuer] Koeppl. "Learning Models of Behavior From Demonstration and Through Interaction / Adrian Šošić ; Abdelhak M. Zoubir, Heinz Koeppl." Darmstadt : Universitäts- und Landesbibliothek Darmstadt, 2018. http://d-nb.info/1169825737/34.

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Bozcuoglu, Asil Kaan. "A Developmental Grasp Learning Scheme For Humanoid Robots." Master's thesis, METU, 2012. http://etd.lib.metu.edu.tr/upload/12614835/index.pdf.

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While an infant is learning to grasp, there are two key processes that she uses for leading a successful development. In the first process, infants use an intuitional approach where the hand is moved towards the object to create an initial contact regardless of the object properties. The contact is followed by a tactile grasping phase where the object is enclosed by the hand. This intuitive grasping behavior leads an grasping mechanism, which utilizes visual input and incorporates this into the grasp plan. The second process is called scaffolding, a guidance by stating how to accomplish the ta

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Narayanan, Krishna Kumar [Verfasser], Torsten [Akademischer Betreuer] Bertram, and Martin [Gutachter] Mönnigmann. "Learning vision based mobile robot behaviors from demonstration / Krishna Kumar Narayanan. Betreuer: Torsten Bertram. Gutachter: Martin Mönnigmann." Dortmund : Universitätsbibliothek Dortmund, 2015. http://d-nb.info/1112041745/34.

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Narayanan, Krishna Kumar Verfasser], Torsten [Akademischer Betreuer] [Bertram, and Martin [Gutachter] Mönnigmann. "Learning vision based mobile robot behaviors from demonstration / Krishna Kumar Narayanan. Betreuer: Torsten Bertram. Gutachter: Martin Mönnigmann." Dortmund : Universitätsbibliothek Dortmund, 2015. http://d-nb.info/1112041745/34.

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Mattisson, Morgan, and Emil Persson. "Från demonstration till individuell kunskap : en studie om samspelet mellan kommunikation och inlärning med fokus på ämnet slöjd." Thesis, Linköpings universitet, Institutionen för kultur och samhälle, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-165707.

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I denna studie försöker vi finna svar på hur en praktisk lärardemonstration kan utföras på ett pedagogiskt vis. Vi undersöker även övergången från den stund som domineras av läraren till den stund som utmärks av individuellt elevarbete. Skolämnet som studien utgår ifrån är slöjd men vi tror att de utmaningar och resultat som framkommer kan appliceras även på andra skolämnen. Från svar på enkäter riktade till elever och intervjuer utförda med både lärare och elever framkommer att ett flertal aspekter spelar in för ett framgångsrikt lärande knutet till demonstrationer. Lärarens kapacitet och fö

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Cakmak, Maya. "Guided teaching interactions with robots: embodied queries and teaching heuristics." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/44734.

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The vision of personal robot assistants continues to become more realistic with technological advances in robotics. The increase in the capabilities of robots, presents boundless opportunities for them to perform useful tasks for humans. However, it is not feasible for engineers to program robots for all possible uses. Instead, we envision general-purpose robots that can be programmed by their end-users. Learning from Demonstration (LfD), is an approach that allows users to program new capabilities on a robot by demonstrating what is required from the robot. Although LfD has become an establ

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LA, TONA Giuseppe. "An Architecture for Observational Learning." Doctoral thesis, Università degli Studi di Palermo, 2014. http://hdl.handle.net/10447/91227.

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In this thesis I present an architecture that learns new skills through observation and adapts to the environment through situated experience in the world. Such an architectural growth is bootstrapped from a minimal initial knowledge and the architecture itself is built around the biologically-inspired notion of internal models. The key idea, supported by findings in cognitive neuroscience, is that the same internal models used in overt goal-directed action execution can be covertly re-enacted in simulation to observe and understand the actions of others. The system applies these concepts to

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Billing, Erik. "Cognition Rehearsed : Recognition and Reproduction of Demonstrated Behavior." Doctoral thesis, Umeå universitet, Institutionen för datavetenskap, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-50980.

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The work presented in this dissertation investigates techniques for robot Learning from Demonstration (LFD). LFD is a well established approach where the robot is to learn from a set of demonstrations. The dissertation focuses on LFD where a human teacher demonstrates a behavior by controlling the robot via teleoperation. After demonstration, the robot should be able to reproduce the demonstrated behavior under varying conditions. In particular, the dissertation investigates techniques where previous behavioral knowledge is used as bias for generalization of demonstrations. The primary contrib

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Rosado, José Fernando Fachada. "Adaptive biped locomotion from a single demonstration using motion primitives." Doctoral thesis, Universidade de Aveiro, 2016. http://hdl.handle.net/10773/17303.

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Doutoramento em Engenharia Eletrotécnica<br>Este trabalho aborda o problema de capacidade de imitação da locomoção humana através da utilização de trajetórias de baixo nível codificadas com primitivas de movimento e utilizá-las para depois generalizar para novas situações, partindo apenas de uma demonstração única. Assim, nesta linha de pensamento, os principais objetivos deste trabalho são dois: o primeiro é analisar, extrair e codificar demonstrações efetuadas por um humano, obtidas por um sistema de captura de movimento de forma a modelar tarefas de locomoção bípede. Contudo, esta tr

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Cobo, Rus Luis Carlos. "Leveraging attention focus for effective reinforcement learning in complex domains." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/47618.

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One of the hardest challenges in the field of machine learning is to build agents, such as robotic assistants in homes and hospitals, that can autonomously learn new tasks that they were not pre-programmed to tackle, without the intervention of an engineer. Reinforcement learning (RL) and learning from demonstration (LfD) are popular approaches for task learning, but they are often ineffective in high-dimensional domains unless provided with either a great deal of problem-specific domain information or a carefully crafted representation of the state and dynamics of the world. Unfortunately, au

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Smith, Joshua L. "Effects of Video Modeling on Skill Acquisition in Learning the Golf Swing." Diss., CLICK HERE for online access, 2004. http://contentdm.lib.byu.edu/ETD/image/etd632.pdf.

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Hagos, Tesfamichael Marikos. "Estimation of phases for compliant motion : Auto-regressive HMM, multi-class logistic regression, Learning from Demonstration (LfD), Gradient descent optimization." Thesis, Luleå tekniska universitet, Rymdteknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-65613.

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Pereira, Renato de Pontes. "HIGMN : an IGMN-based hierarchical architecture and its applications for robotic tasks." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2013. http://hdl.handle.net/10183/80752.

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Abstract:

O recente campo de Deep Learning introduziu a área de Aprendizagem de Máquina novos métodos baseados em representações distribuídas e abstratas dos dados de treinamento ao longo de estruturas hierárquicas. A organização hierárquica de camadas permite que esses métodos guardem informações distribuídas sobre os sinais sensoriais e criem conceitos com diferentes níveis de abstração para representar os dados de entrada. Este trabalho investiga o impacto de uma estrutura hierárquica inspirada pelas ideias apresentadas em Deep Learning, e com base na Incremental Gaussian Mixture Network (IGMN), uma

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Masini, Douglas Eugene. "The Accidental Experts: A Demonstration of the Existence, Utility, and Emergence of Implicitly Learned Tacit Knowledge in Assistive-Augmentative Technology Users." [Johnson City, Tenn. : East Tennessee State University], 2001. http://etd-submit.etsu.edu/etd/theses/available/etd-1108101-170203/unrestricted/masinid120601.pdf.

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Silver, David. "Learning Preference Models for Autonomous Mobile Robots in Complex Domains." Research Showcase @ CMU, 2010. http://repository.cmu.edu/dissertations/551.

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Achieving robust and reliable autonomous operation even in complex unstructured environments is a central goal of field robotics. As the environments and scenarios to which robots are applied have continued to grow in complexity, so has the challenge of properly defining preferences and tradeoffs between various actions and the terrains they result in traversing. These definitions and parameters encode the desired behavior of the robot; therefore their correctness is of the utmost importance. Current manual approaches to creating and adjusting these preference models and cost functions have pr

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Akgun, Baris. "Action Recognition Through Action Generation." Master's thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/12612306/index.pdf.

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This thesis investigates how a robot can use action generation mechanisms to recognize the action of an observed actor in an on-line manner i.e., before the completion of the action. Towards this end, Dynamic Movement Primitives (DMP), an action generation method proposed for imitation, are modified to recognize the actions of an actor. Specifically, a human actor performed three different reaching actions to two different objects. Three DMP&#039<br>s, each corresponding to a different reaching action, were trained using this data. The proposed method used an object-centered coordinate system

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