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Littérature scientifique sur le sujet « Demonstration Learning »

Auteur : Grafiati

Publié le 7 juin 2025

Mis à jour le 2 août 2025

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Articles de revues sur le sujet "Demonstration Learning"

Junaesih, Junaesih, Ajeng Kurniawati, and Humairoh Humairoh. "Application of the Demonstration Learning Model to Improve Student Learning Outcomes in Class 4 Social Sciences Lessons at State Elementary School 15 Serang." Jurnal Primagraha 3, no. 02 (2022): 53–63. http://dx.doi.org/10.59605/jp.v3i02.671.

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This research discusses the implementation of the demonstration method to improve student learning outcomes. Library research method with a qualitative descriptive approach. The results of this research from several theses and journals show that the demonstration method can improve student learning outcomes. Demonstration is a mode of presenting lessons by demonstrating and demonstrating to students a particular process, situation or object, either actually or just an imitation. The purpose of teaching using the demonstration model is a way of presenting lessons by demonstrating and demonstrat

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Defi Antika and Kharis Sulaiman Hasri. "Implementation of Silent Demonstration Learning Strategy to Improve Elementary Madrasah Students' Learning Outcomes." Journal of Contemporary Islamic Primary Education 2, no. 3 (2024): 164–69. https://doi.org/10.61253/jcipe.v2i3.299.

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This study aims to describe the implementation of Silent Demonstration for MI/SD students and how this activity can integrate students to think critically, creatively, communicatively, and collaboratively. This research uses a type of qualitative research with descriptive methods. Descriptive research, that is, research conducted to determine the value of the independent variable, either one variable or more (independent) without making comparisons or connecting with other variables. Qualitative collection techniques usually use observation, interviews, and documentation. The demonstration str

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Chernova, S., and M. Veloso. "Interactive Policy Learning through Confidence-Based Autonomy." Journal of Artificial Intelligence Research 34 (January 23, 2009): 1–25. http://dx.doi.org/10.1613/jair.2584.

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We present Confidence-Based Autonomy (CBA), an interactive algorithm for policy learning from demonstration. The CBA algorithm consists of two components which take advantage of the complimentary abilities of humans and computer agents. The first component, Confident Execution, enables the agent to identify states in which demonstration is required, to request a demonstration from the human teacher and to learn a policy based on the acquired data. The algorithm selects demonstrations based on a measure of action selection confidence, and our results show that using Confident Execution the agen

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Birlson, O., and M. Singleton. "The Demonstration Kitchen: Gauging Participant Learning in Cooking Demonstrations." Journal of the Academy of Nutrition and Dietetics 117, no. 10 (2017): A139. http://dx.doi.org/10.1016/j.jand.2017.08.076.

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Zha, Yantian, Lin Guan, and Subbarao Kambhampati. "Learning from Ambiguous Demonstrations with Self-Explanation Guided Reinforcement Learning." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 9 (2024): 10395–403. http://dx.doi.org/10.1609/aaai.v38i9.28907.

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Our work aims at efficiently leveraging ambiguous demonstrations for the training of a reinforcement learning (RL) agent. An ambiguous demonstration can usually be interpreted in multiple ways, which severely hinders the RL agent from learning stably and efficiently. Since an optimal demonstration may also suffer from being ambiguous, previous works that combine RL and learning from demonstration (RLfD works) may not work well. Inspired by how humans handle such situations, we propose to use self-explanation (an agent generates explanations for itself) to recognize valuable high-level relation

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Mantahari Pohan. "Upaya Meningkatan Hasil Belajar IPA Melalui Model Pembelajaran Demonstarasi Pada Siswa Kelas VI SDN 027 Danau Lancang Kecamatan Tapung Hulu Kabupaten Kampar." Journal Innovation In Education 1, no. 1 (2023): 06–12. http://dx.doi.org/10.59841/inoved.v2i1.993.

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Demonstration is a very effective model, because it helps students to find answers on their own based on correct facts or data. The demonstration method is a method of presenting lessons by demonstrating and demonstrating to students a particular process, situation or object, either actually or just an imitation. As a presentation method, demonstration cannot be separated from verbal explanation by the teacher. Based on the results of data analysis and discussion, it can show some of the progress achieved during learning, both through classical learning, group learning results, and learning ou

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Gorbenko, Anna, and Vladimir Popov. "Mechanical Research with Intelligent Avatars for Robot Learning from Demonstration." Advanced Materials Research 952 (May 2014): 287–90. http://dx.doi.org/10.4028/www.scientific.net/amr.952.287.

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It is well known that learning from demonstration has many attractive points for both learner and teacher. However, the quality of such learning depends crucially from the system of understanding of human demonstrations. In this paper, we consider an approach to design intelligent avatars for robot learning from demonstration. Also, we present experimental results for intelligent avatar mediated learning from demonstration.

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Zhang, Ning, Tao Qi, and Yongjia Zhao. "Real-Time Learning and Recognition of Assembly Activities Based on Virtual Reality Demonstration." Sensors 21, no. 18 (2021): 6201. http://dx.doi.org/10.3390/s21186201.

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Teaching robots to learn through human demonstrations is a natural and direct method, and virtual reality technology is an effective way to achieve fast and realistic demonstrations. In this paper, we construct a virtual reality demonstration system that uses virtual reality equipment for assembly activities demonstration, and using the motion data of the virtual demonstration system, the human demonstration is deduced into an activity sequence that can be performed by the robot. Through experimentation, the virtual reality demonstration system in this paper can achieve a 95% correct rate of a

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Laka, Diana Siska. "THE INFLUENCE OF THE DEMONSTRATION METHOD ON THE LEARNING MOTIVATION OF CLASS VI STUDENTS OFSDN 078553 ORLIN, SOUTH NIAS REGENCY ONINDONESIAN LANGUAGE SUBJECTS." Journal of Learning on History and Social Sciences 1, no. 4 (2024): 36–44. https://doi.org/10.61796/ejlhss.v1i4.509.

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The demonstration method is very important in teaching because it makes abstract concepts more concrete and easier for students to understand. In addition, demonstrations involve students directly in the learning process. They can see, hear, and often participate in demonstrations, which helps improve information retention. Meanwhile, motivating students is one of the most important aspects of effective teaching. This research aims to determine the effect of the demonstration method on the learning motivation of class VI students at SDN 078553 Orlin in Indonesian language subjects. The method

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Billard, Aude, and Roland Siegwart. "Robot learning from demonstration." Robotics and Autonomous Systems 47, no. 2-3 (2004): 65–67. http://dx.doi.org/10.1016/j.robot.2004.03.001.

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Thèses sur le sujet "Demonstration Learning"

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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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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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. Cataloged from PDF version of thesis. Includes bibliographical references (pages 123-132). 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. This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. 35 Cataloged from student-submitted PDF version of thesis. Includes bibliographical references (pages 94-98). 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. This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. Cataloged from student-submitted PDF version of thesis. Includes bibliographical references (pages 79-82). 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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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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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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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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Livres sur le sujet "Demonstration Learning"

California) International Electric Vehicle Symposium (26 2012 Los Angeles. Final results from U.S. FCEV learning demonstration. National Renewable Energy Laboratory, 2012.

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I, Brown Roy, and Samuels Marilyn, eds. Research and practice in learning difficulties: A demonstration model. Lugus, 1989.

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Dryden, Windy. Rational emotive behaviour therapy: Learning from demonstration sessions. Whurr Publishers, 1996.

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Wipke, Keith. Next steps for the FCEV learning demonstration project. National Renewable Energy Laboratory, 2011.

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B, Wipke Keith, Sprik Sam, and National Renewable Energy Laboratory (U.S.), eds. FCV learning demonstration: Factors affecting fuel cell degradation. National Renewable Energy Laboratory, 2008.

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Wipke, Keith. Status of U.S. FCEV and infrastructure learning demonstration project. National Renewable Energy Laboratory, 2011.

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CA) ZEV Technology Symposium (2009 Sacramento. National fuel cell vehicle learning demonstration: Gen 2 progress. National Renewable Energy Laboratory, U.S. Dept. of Energy, 2009.

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Wipke, Keith B. National FCEV learning demonstration: Winter 2011, composite data products. National Renewable Energy Laboratory, 2012.

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Buron, Larry. The Lifelong Learning Demonstration: Final evaluation report on the experimental site. U.S. Dept. of Labor, Employment and Training Administration, 1999.

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Buron, Larry. The Lifelong Learning Demonstration: Final evaluation report on the experimental site. U.S. Dept. of Labor, Employment and Training Administration, 1999.

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Chapitres de livres sur le sujet "Demonstration Learning"

Mohammad, Yasser, and Toyoaki Nishida. "Learning from Demonstration." In Advanced Information and Knowledge Processing. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-25232-2_13.

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Webb, Geoffrey I., Claude Sammut, Claudia Perlich, et al. "Learning By Demonstration." In Encyclopedia of Machine Learning. Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_447.

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Blackburn, Barbara R., and Abbigail Armstrong. "Demonstration of Learning." In Rigor in the 6–12 Math and Science Classroom. Routledge, 2018. http://dx.doi.org/10.4324/9781315176055-5.

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Blackburn, Barbara R., and Melissa Miles. "Demonstration of Learning." In Rigor in the 6–12 ELA and Social Studies Classroom. Routledge, 2018. http://dx.doi.org/10.4324/9781351062060-5.

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Blackburn, Barbara R., and Melissa Miles. "Demonstration of Learning." In Rigor in the K–5 ELA and Social Studies Classroom. Eye on Education, 2019. http://dx.doi.org/10.4324/9780429486005-5.

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Blackburn, Barbara R., and Abbigail Armstrong. "Demonstration of Learning." In Rigor in the K–5 Math and Science Classroom. Eye on Education, 2019. http://dx.doi.org/10.4324/9780429324994-5.

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Calinon, Sylvain. "Learning from Demonstration (Programming by Demonstration)." In Encyclopedia of Robotics. Springer Berlin Heidelberg, 2018. http://dx.doi.org/10.1007/978-3-642-41610-1_27-1.

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Zeugmann, Thomas, Pascal Poupart, James Kennedy, et al. "Programming by Demonstration." In Encyclopedia of Machine Learning. Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_673.

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Billing, Erik A., Thomas Hellström, and Lars-Erik Janlert. "Predictive Learning from Demonstration." In Communications in Computer and Information Science. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19890-8_14.

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Chernova, Sonia. "Robot Learning from Demonstration." In Encyclopedia of the Sciences of Learning. Springer US, 2012. http://dx.doi.org/10.1007/978-1-4419-1428-6_673.

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Actes de conférences sur le sujet "Demonstration Learning"

Bilal, Muhammad, Nir Lipovetzky, Denny Oetomo, and Wafa Johal. "Beyond Success: Quantifying Demonstration Quality in Learning from Demonstration." In 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2024. https://doi.org/10.1109/iros58592.2024.10802187.

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Qin, Chengwei, Aston Zhang, Chen Chen, Anirudh Dagar, and Wenming Ye. "In-Context Learning with Iterative Demonstration Selection." In Findings of the Association for Computational Linguistics: EMNLP 2024. Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.findings-emnlp.438.

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Blau, Tom, Philippe Morere, and Gilad Francis. "Learning from Demonstration without Demonstrations." In 2021 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2021. http://dx.doi.org/10.1109/icra48506.2021.9561119.

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Pettersson, Max, Florian Westphal, and Maria Riveiro. "Exploring demonstration pre-training with improved Deep Q-learning." In 14th Scandinavian Conference on Artificial Intelligence SCAI 2024, June 10-11, 2024, Jönköping, Sweden. Linköping University Electronic Press, 2024. http://dx.doi.org/10.3384/ecp208008.

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This study explores the effects of incorporating demonstrations as pre-training of an improved Deep Q-Network (DQN). Inspiration is taken from methods such as Deep Q-learning from Demonstrations (DQfD), but instead of retaining the demonstrations throughout the training, the performance and behavioral effects of the policy when using demonstrations solely as pre-training are studied. A comparative experiment is performed on two game environments, Gymnasium's Car Racing and Atari Space Invaders. While demonstration pre-training in Car Racing shows improved learning efficacy, as indicated by hig

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Fitzgerald, Tesca, and Andrea L. Thomaz. "Skill Demonstration Transfer for Learning from Demonstration." In HRI '15: ACM/IEEE International Conference on Human-Robot Interaction. ACM, 2015. http://dx.doi.org/10.1145/2701973.2702728.

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Bondarenko, Yevhen, Simone Luca Pizzagalli, Vladimir Kuts, Eduard Petlenkov, and Tauno Otto. "Digital Twin Based Learning From Demonstration System for Industrial Robots." In ASME 2023 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2023. http://dx.doi.org/10.1115/imece2023-113240.

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Abstract Learning from Demonstration (LfD) is an approach to robot programming where the machine aims to replicate the task presented by a human without being explicitly programmed to execute this task. While being an effective way to create complex robot routines even for users without coding skills, most LfD implementations heavily rely on sensors for the robot to capture the state of the surrounding world and the task being demonstrated. In this research paper we offer an alternative LfD approach based on a fully simulated 3D environment. We demonstreate how simulation can eliminate the nee

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Paleja, Rohan, and Matthew Gombolay. "Heterogeneous Learning from Demonstration." In 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI). IEEE, 2019. http://dx.doi.org/10.1109/hri.2019.8673267.

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Wen, Min, Ivan Papusha, and Ufuk Topcu. "Learning from Demonstrations with High-Level Side Information." In Twenty-Sixth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/426.

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We consider the problem of learning from demonstration, where extra side information about the demonstration is encoded as a co-safe linear temporal logic formula. We address two known limitations of existing methods that do not account for such side information. First, the policies that result from existing methods, while matching the expected features or likelihood of the demonstrations, may still be in conflict with high-level objectives not explicit in the demonstration trajectories. Second, existing methods fail to provide a priori guarantees on the out-of-sample generalization performanc

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Peng, Peixi, Junliang Xing, and Lili Cao. "Hybrid Learning for Multi-agent Cooperation with Sub-optimal Demonstrations." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/420.

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This paper aims to learn multi-agent cooperation where each agent performs its actions in a decentralized way. In this case, it is very challenging to learn decentralized policies when the rewards are global and sparse. Recently, learning from demonstrations (LfD) provides a promising way to handle this challenge. However, in many practical tasks, the available demonstrations are often sub-optimal. To learn better policies from these sub-optimal demonstrations, this paper follows a centralized learning and decentralized execution framework and proposes a novel hybrid learning method based on m

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Tobagi, Fouad A. "On-Line distance learning (demonstration)." In the second ACM international conference. ACM Press, 1994. http://dx.doi.org/10.1145/192593.197379.

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Rapports d'organisations sur le sujet "Demonstration Learning"

Wipke, K., S. Sprik, J. Kurtz, and H. Thomas. Learning Demonstration Progress Report -- September 2007. Office of Scientific and Technical Information (OSTI), 2007. http://dx.doi.org/10.2172/919968.

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Wipke, K., S. Sprik, and J. Kurtz. Learning Demonstration Progress Report -- Spring 2008. Office of Scientific and Technical Information (OSTI), 2008. http://dx.doi.org/10.2172/928251.

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Wipke, K., S. Sprik, H. Thomas, C. Welch, and J. Kurtz. Learning Demonstration Interim Progress Report -- Summer 2007. Office of Scientific and Technical Information (OSTI), 2007. http://dx.doi.org/10.2172/912954.

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Wipke, K., S. Spirk, J. Kurtz, and T. Ramsden. Learning Demonstration Interim Progress Report -- July 2010. Office of Scientific and Technical Information (OSTI), 2010. http://dx.doi.org/10.2172/988602.

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Wipke, K., S. Sprik, J. Kurtz, and T. Ramsden. National FCEV Learning Demonstration: Spring 2011 Composite Data Products. Office of Scientific and Technical Information (OSTI), 2011. http://dx.doi.org/10.2172/1013895.

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Wipke, K., S. Sprik, J. Kurtz, T. Ramsden, C. Ainscough, and G. Saur. National Fuel Cell Electric Vehicle Learning Demonstration Final Report. Office of Scientific and Technical Information (OSTI), 2012. http://dx.doi.org/10.2172/1219731.

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Wipke, Keith, Sam Sprik, Jennifer Kurtz, Todd Ramsden, Chris Ainscough, and Genevieve Saur. National Fuel Cell Electric Vehicle Learning Demonstration Final Report. Office of Scientific and Technical Information (OSTI), 2012. http://dx.doi.org/10.2172/1051147.

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Wipke, Keith, Sam Sprik, Jennifer Kurtz, Todd Ramsden, Chris Ainscough, and Genevieve Saur. Fall 2011 Composite Data Products: National FCEV Learning Demonstration. Office of Scientific and Technical Information (OSTI), 2011. http://dx.doi.org/10.2172/1029425.

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Wipke, Keith, Sam Sprik, Jennifer Kurtz, Todd Ramsden, Chris Ainscough, and Genevieve Saur. National FCEV Learning Demonstration: Winter 2011 Composite Data Products. Office of Scientific and Technical Information (OSTI), 2012. http://dx.doi.org/10.2172/1036036.

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Silver, David, J. A. Bagnell, and Anthony Stentz. Learning from Demonstration for Autonomous Navigation in Complex Unstructured Terrain. Defense Technical Information Center, 2010. http://dx.doi.org/10.21236/ada525288.

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