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You searched for subject:(learning from demonstration). Showing records 1 – 30 of 39 total matches.

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Georgia Tech

1. Rana, Muhammad Asif. Methods for Teaching Diverse Robot Skills: Leveraging Priors, Geometry, and Dynamics.

Degree: PhD, Electrical and Computer Engineering, 2020, Georgia Tech

 Functioning in the real world requires robots to reason about and generate motions for execution of complex tasks, in potentially unstructured and dynamic environments. Early… (more)

Subjects/Keywords: learning from demonstration; robot learning

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Rana, M. A. (2020). Methods for Teaching Diverse Robot Skills: Leveraging Priors, Geometry, and Dynamics. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/64106

Chicago Manual of Style (16th Edition):

Rana, Muhammad Asif. “Methods for Teaching Diverse Robot Skills: Leveraging Priors, Geometry, and Dynamics.” 2020. Doctoral Dissertation, Georgia Tech. Accessed April 13, 2021. http://hdl.handle.net/1853/64106.

MLA Handbook (7th Edition):

Rana, Muhammad Asif. “Methods for Teaching Diverse Robot Skills: Leveraging Priors, Geometry, and Dynamics.” 2020. Web. 13 Apr 2021.

Vancouver:

Rana MA. Methods for Teaching Diverse Robot Skills: Leveraging Priors, Geometry, and Dynamics. [Internet] [Doctoral dissertation]. Georgia Tech; 2020. [cited 2021 Apr 13]. Available from: http://hdl.handle.net/1853/64106.

Council of Science Editors:

Rana MA. Methods for Teaching Diverse Robot Skills: Leveraging Priors, Geometry, and Dynamics. [Doctoral Dissertation]. Georgia Tech; 2020. Available from: http://hdl.handle.net/1853/64106


University of Illinois – Urbana-Champaign

2. Aghasadeghi, Navid. Inverse optimal control for differentially flat systems with application to lower-limb prosthetic devices.

Degree: PhD, Electrical & Computer Engr, 2015, University of Illinois – Urbana-Champaign

 Powered prosthetic devices have shown to be capable of restoring natural gait to amputees. However, the commercialization of these devices is faced by some challenges,… (more)

Subjects/Keywords: Prosthetic control; Learning from demonstration

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APA (6th Edition):

Aghasadeghi, N. (2015). Inverse optimal control for differentially flat systems with application to lower-limb prosthetic devices. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/78483

Chicago Manual of Style (16th Edition):

Aghasadeghi, Navid. “Inverse optimal control for differentially flat systems with application to lower-limb prosthetic devices.” 2015. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed April 13, 2021. http://hdl.handle.net/2142/78483.

MLA Handbook (7th Edition):

Aghasadeghi, Navid. “Inverse optimal control for differentially flat systems with application to lower-limb prosthetic devices.” 2015. Web. 13 Apr 2021.

Vancouver:

Aghasadeghi N. Inverse optimal control for differentially flat systems with application to lower-limb prosthetic devices. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2015. [cited 2021 Apr 13]. Available from: http://hdl.handle.net/2142/78483.

Council of Science Editors:

Aghasadeghi N. Inverse optimal control for differentially flat systems with application to lower-limb prosthetic devices. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2015. Available from: http://hdl.handle.net/2142/78483


Washington State University

3. [No author]. Knowledge Transfer in Reinforcement Learning: How agents should benefit from prior knowledge .

Degree: 2019, Washington State University

 Reinforcement learning (RL) has had many successes in different tasks, but in practice, it often requires significant amounts of data or training time to learn… (more)

Subjects/Keywords: Computer science; Learning from Demonstration; Machine Learning; Reinforcement Learning; Transfer Learning

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APA (6th Edition):

author], [. (2019). Knowledge Transfer in Reinforcement Learning: How agents should benefit from prior knowledge . (Thesis). Washington State University. Retrieved from http://hdl.handle.net/2376/17895

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

author], [No. “Knowledge Transfer in Reinforcement Learning: How agents should benefit from prior knowledge .” 2019. Thesis, Washington State University. Accessed April 13, 2021. http://hdl.handle.net/2376/17895.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

author], [No. “Knowledge Transfer in Reinforcement Learning: How agents should benefit from prior knowledge .” 2019. Web. 13 Apr 2021.

Vancouver:

author] [. Knowledge Transfer in Reinforcement Learning: How agents should benefit from prior knowledge . [Internet] [Thesis]. Washington State University; 2019. [cited 2021 Apr 13]. Available from: http://hdl.handle.net/2376/17895.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

author] [. Knowledge Transfer in Reinforcement Learning: How agents should benefit from prior knowledge . [Thesis]. Washington State University; 2019. Available from: http://hdl.handle.net/2376/17895

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Technology, Sydney

4. Raza, Syed Ali. Computational reinforcement learning using rewards from human feedback.

Degree: 2018, University of Technology, Sydney

 A promising method of learning from human feedback is reward shaping, where a robot is trained via human-delivered instantaneous rewards. The existing approach, which requires… (more)

Subjects/Keywords: Reinforcement learning.; Human feedback.; Reward shaping.; Learning from demonstration.

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APA (6th Edition):

Raza, S. A. (2018). Computational reinforcement learning using rewards from human feedback. (Thesis). University of Technology, Sydney. Retrieved from http://hdl.handle.net/10453/128007

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Raza, Syed Ali. “Computational reinforcement learning using rewards from human feedback.” 2018. Thesis, University of Technology, Sydney. Accessed April 13, 2021. http://hdl.handle.net/10453/128007.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Raza, Syed Ali. “Computational reinforcement learning using rewards from human feedback.” 2018. Web. 13 Apr 2021.

Vancouver:

Raza SA. Computational reinforcement learning using rewards from human feedback. [Internet] [Thesis]. University of Technology, Sydney; 2018. [cited 2021 Apr 13]. Available from: http://hdl.handle.net/10453/128007.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Raza SA. Computational reinforcement learning using rewards from human feedback. [Thesis]. University of Technology, Sydney; 2018. Available from: http://hdl.handle.net/10453/128007

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Delft University of Technology

5. Meccanici, Floris (author). Teleoperated online Learning from Demonstration in a partly unknown environment: using a semi-autonomous care robot.

Degree: 2021, Delft University of Technology

The general approach to generate collision free motion in a constraint environment is to use path planners, which demand a known environment and potentially fail… (more)

Subjects/Keywords: Learning from Demonstration; Online Learning; Human-Robot Interaction; Unknown Environment

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APA (6th Edition):

Meccanici, F. (. (2021). Teleoperated online Learning from Demonstration in a partly unknown environment: using a semi-autonomous care robot. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:346f2743-121c-419f-afa2-96c8745d1003

Chicago Manual of Style (16th Edition):

Meccanici, Floris (author). “Teleoperated online Learning from Demonstration in a partly unknown environment: using a semi-autonomous care robot.” 2021. Masters Thesis, Delft University of Technology. Accessed April 13, 2021. http://resolver.tudelft.nl/uuid:346f2743-121c-419f-afa2-96c8745d1003.

MLA Handbook (7th Edition):

Meccanici, Floris (author). “Teleoperated online Learning from Demonstration in a partly unknown environment: using a semi-autonomous care robot.” 2021. Web. 13 Apr 2021.

Vancouver:

Meccanici F(. Teleoperated online Learning from Demonstration in a partly unknown environment: using a semi-autonomous care robot. [Internet] [Masters thesis]. Delft University of Technology; 2021. [cited 2021 Apr 13]. Available from: http://resolver.tudelft.nl/uuid:346f2743-121c-419f-afa2-96c8745d1003.

Council of Science Editors:

Meccanici F(. Teleoperated online Learning from Demonstration in a partly unknown environment: using a semi-autonomous care robot. [Masters Thesis]. Delft University of Technology; 2021. Available from: http://resolver.tudelft.nl/uuid:346f2743-121c-419f-afa2-96c8745d1003


University of Toronto

6. Louie, Wing-Yue Geoffrey. A Learning Based Robot Interaction System for Multi-User Activities.

Degree: PhD, 2017, University of Toronto

 The population of the world is rapidly aging and there is presently an increasing demand for residential care facilities to provide care for older adults.… (more)

Subjects/Keywords: Human-Robot Interaction; Learning from Demonstration; Socially Assistive Robotics; 0771

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APA (6th Edition):

Louie, W. G. (2017). A Learning Based Robot Interaction System for Multi-User Activities. (Doctoral Dissertation). University of Toronto. Retrieved from http://hdl.handle.net/1807/80772

Chicago Manual of Style (16th Edition):

Louie, Wing-Yue Geoffrey. “A Learning Based Robot Interaction System for Multi-User Activities.” 2017. Doctoral Dissertation, University of Toronto. Accessed April 13, 2021. http://hdl.handle.net/1807/80772.

MLA Handbook (7th Edition):

Louie, Wing-Yue Geoffrey. “A Learning Based Robot Interaction System for Multi-User Activities.” 2017. Web. 13 Apr 2021.

Vancouver:

Louie WG. A Learning Based Robot Interaction System for Multi-User Activities. [Internet] [Doctoral dissertation]. University of Toronto; 2017. [cited 2021 Apr 13]. Available from: http://hdl.handle.net/1807/80772.

Council of Science Editors:

Louie WG. A Learning Based Robot Interaction System for Multi-User Activities. [Doctoral Dissertation]. University of Toronto; 2017. Available from: http://hdl.handle.net/1807/80772


University of California – Santa Cruz

7. Leece, Michael Ong. Learning Hierarchical Abstractions from Human Demonstrations for Application-Scale Domains.

Degree: Computer Science, 2018, University of California – Santa Cruz

 As the collection of data becomes more and more commonplace, it unlocks new approaches to old problems in the field of artificial intelligence. Much of… (more)

Subjects/Keywords: Artificial intelligence; Computer science; Hierarchical planning; Learning from demonstration; Machine learning; RTS games

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APA (6th Edition):

Leece, M. O. (2018). Learning Hierarchical Abstractions from Human Demonstrations for Application-Scale Domains. (Thesis). University of California – Santa Cruz. Retrieved from http://www.escholarship.org/uc/item/6gs3t5xn

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Leece, Michael Ong. “Learning Hierarchical Abstractions from Human Demonstrations for Application-Scale Domains.” 2018. Thesis, University of California – Santa Cruz. Accessed April 13, 2021. http://www.escholarship.org/uc/item/6gs3t5xn.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Leece, Michael Ong. “Learning Hierarchical Abstractions from Human Demonstrations for Application-Scale Domains.” 2018. Web. 13 Apr 2021.

Vancouver:

Leece MO. Learning Hierarchical Abstractions from Human Demonstrations for Application-Scale Domains. [Internet] [Thesis]. University of California – Santa Cruz; 2018. [cited 2021 Apr 13]. Available from: http://www.escholarship.org/uc/item/6gs3t5xn.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Leece MO. Learning Hierarchical Abstractions from Human Demonstrations for Application-Scale Domains. [Thesis]. University of California – Santa Cruz; 2018. Available from: http://www.escholarship.org/uc/item/6gs3t5xn

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Southern California

8. Koenig, Nathan. Robot life-long task learning from human demonstrations: a Bayesian approach.

Degree: PhD, Computer Science (Robotics and Automation), 2013, University of Southern California

 Programming a robot to act intelligently is a challenging endeavor that is beyond the skill level of most people. Trained roboticists generally program robots for… (more)

Subjects/Keywords: robotics; life-long learning; influence diagrams; bayesian networks; teaching; learning from demonstration

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APA (6th Edition):

Koenig, N. (2013). Robot life-long task learning from human demonstrations: a Bayesian approach. (Doctoral Dissertation). University of Southern California. Retrieved from http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/222240/rec/5611

Chicago Manual of Style (16th Edition):

Koenig, Nathan. “Robot life-long task learning from human demonstrations: a Bayesian approach.” 2013. Doctoral Dissertation, University of Southern California. Accessed April 13, 2021. http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/222240/rec/5611.

MLA Handbook (7th Edition):

Koenig, Nathan. “Robot life-long task learning from human demonstrations: a Bayesian approach.” 2013. Web. 13 Apr 2021.

Vancouver:

Koenig N. Robot life-long task learning from human demonstrations: a Bayesian approach. [Internet] [Doctoral dissertation]. University of Southern California; 2013. [cited 2021 Apr 13]. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/222240/rec/5611.

Council of Science Editors:

Koenig N. Robot life-long task learning from human demonstrations: a Bayesian approach. [Doctoral Dissertation]. University of Southern California; 2013. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/222240/rec/5611


Loughborough University

9. Al-Yacoub, Ali. Robotic learning of force-based industrial manipulation tasks.

Degree: PhD, 2019, Loughborough University

 Even with the rapid technological advancements, robots are still not the most comfortable machines to work with. Firstly, due to the separation of the robot… (more)

Subjects/Keywords: Mechanical Engineering not elsewhere classified; programming-by-demonstration; Learning-from-Demonstration; Industrial Robots; Force/Torque Signal; Human-Robot-Collaboration

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APA (6th Edition):

Al-Yacoub, A. (2019). Robotic learning of force-based industrial manipulation tasks. (Doctoral Dissertation). Loughborough University. Retrieved from https://doi.org/10.26174/thesis.lboro.8295767.v1 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.785247

Chicago Manual of Style (16th Edition):

Al-Yacoub, Ali. “Robotic learning of force-based industrial manipulation tasks.” 2019. Doctoral Dissertation, Loughborough University. Accessed April 13, 2021. https://doi.org/10.26174/thesis.lboro.8295767.v1 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.785247.

MLA Handbook (7th Edition):

Al-Yacoub, Ali. “Robotic learning of force-based industrial manipulation tasks.” 2019. Web. 13 Apr 2021.

Vancouver:

Al-Yacoub A. Robotic learning of force-based industrial manipulation tasks. [Internet] [Doctoral dissertation]. Loughborough University; 2019. [cited 2021 Apr 13]. Available from: https://doi.org/10.26174/thesis.lboro.8295767.v1 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.785247.

Council of Science Editors:

Al-Yacoub A. Robotic learning of force-based industrial manipulation tasks. [Doctoral Dissertation]. Loughborough University; 2019. Available from: https://doi.org/10.26174/thesis.lboro.8295767.v1 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.785247


Texas A&M University

10. Allain, Mitchell Anthony. Shared Control Policies and Task Learning for Hydraulic Earth-Moving Machinery.

Degree: MS, Mechanical Engineering, 2017, Texas A&M University

 This thesis develops a shared control design framework for improving operator efficiency and performance on hydraulic excavation tasks. The framework is based on blended shared… (more)

Subjects/Keywords: shared control; blended shared control; human-machine interface; collaborative robotics; learning from demonstration

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APA (6th Edition):

Allain, M. A. (2017). Shared Control Policies and Task Learning for Hydraulic Earth-Moving Machinery. (Masters Thesis). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/165829

Chicago Manual of Style (16th Edition):

Allain, Mitchell Anthony. “Shared Control Policies and Task Learning for Hydraulic Earth-Moving Machinery.” 2017. Masters Thesis, Texas A&M University. Accessed April 13, 2021. http://hdl.handle.net/1969.1/165829.

MLA Handbook (7th Edition):

Allain, Mitchell Anthony. “Shared Control Policies and Task Learning for Hydraulic Earth-Moving Machinery.” 2017. Web. 13 Apr 2021.

Vancouver:

Allain MA. Shared Control Policies and Task Learning for Hydraulic Earth-Moving Machinery. [Internet] [Masters thesis]. Texas A&M University; 2017. [cited 2021 Apr 13]. Available from: http://hdl.handle.net/1969.1/165829.

Council of Science Editors:

Allain MA. Shared Control Policies and Task Learning for Hydraulic Earth-Moving Machinery. [Masters Thesis]. Texas A&M University; 2017. Available from: http://hdl.handle.net/1969.1/165829


University of South Florida

11. Huang, Yongqiang. Robotic Motion Generation by Using Spatial-Temporal Patterns from Human Demonstrations.

Degree: 2019, University of South Florida

 Robots excel in manufacturing facilities because the tasks are repetitive and do not change. However, when the tasks change, which happens in almost all tasks… (more)

Subjects/Keywords: learning from demonstration; trajectory generation; velocity generation; Artificial Intelligence and Robotics; Computer Sciences; Robotics

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APA (6th Edition):

Huang, Y. (2019). Robotic Motion Generation by Using Spatial-Temporal Patterns from Human Demonstrations. (Thesis). University of South Florida. Retrieved from https://scholarcommons.usf.edu/etd/8373

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Huang, Yongqiang. “Robotic Motion Generation by Using Spatial-Temporal Patterns from Human Demonstrations.” 2019. Thesis, University of South Florida. Accessed April 13, 2021. https://scholarcommons.usf.edu/etd/8373.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Huang, Yongqiang. “Robotic Motion Generation by Using Spatial-Temporal Patterns from Human Demonstrations.” 2019. Web. 13 Apr 2021.

Vancouver:

Huang Y. Robotic Motion Generation by Using Spatial-Temporal Patterns from Human Demonstrations. [Internet] [Thesis]. University of South Florida; 2019. [cited 2021 Apr 13]. Available from: https://scholarcommons.usf.edu/etd/8373.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Huang Y. Robotic Motion Generation by Using Spatial-Temporal Patterns from Human Demonstrations. [Thesis]. University of South Florida; 2019. Available from: https://scholarcommons.usf.edu/etd/8373

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Indiana University

12. Lee, Jangwon. Learning Activities From Human Demonstration Videos .

Degree: 2018, Indiana University

 In this thesis we describe novel computer vision approaches to observe and learn activities from human demonstration videos. We specifically focus on using first-person and… (more)

Subjects/Keywords: Convolutional Neural Network; Intelligent Systems; Learning from Demonstration; Human-Robot Collaboration; Early Recognition

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APA (6th Edition):

Lee, J. (2018). Learning Activities From Human Demonstration Videos . (Thesis). Indiana University. Retrieved from http://hdl.handle.net/2022/22549

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Lee, Jangwon. “Learning Activities From Human Demonstration Videos .” 2018. Thesis, Indiana University. Accessed April 13, 2021. http://hdl.handle.net/2022/22549.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Lee, Jangwon. “Learning Activities From Human Demonstration Videos .” 2018. Web. 13 Apr 2021.

Vancouver:

Lee J. Learning Activities From Human Demonstration Videos . [Internet] [Thesis]. Indiana University; 2018. [cited 2021 Apr 13]. Available from: http://hdl.handle.net/2022/22549.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Lee J. Learning Activities From Human Demonstration Videos . [Thesis]. Indiana University; 2018. Available from: http://hdl.handle.net/2022/22549

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Washington

13. Warrier, Rahul Balakrishna. Inferring human intent in novice human-in-the-loop control tasks.

Degree: PhD, 2018, University of Washington

 Inferring intentions is fundamental to successful interaction among two or more agents. For example, in learning from a teacher the student must be able to… (more)

Subjects/Keywords: Human modeling; Intent estimation; Iterative learning control; Learning from demonstration; Machine learning; System inversion; Mechanical engineering; Robotics; Systems science; Mechanical engineering

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APA (6th Edition):

Warrier, R. B. (2018). Inferring human intent in novice human-in-the-loop control tasks. (Doctoral Dissertation). University of Washington. Retrieved from http://hdl.handle.net/1773/43098

Chicago Manual of Style (16th Edition):

Warrier, Rahul Balakrishna. “Inferring human intent in novice human-in-the-loop control tasks.” 2018. Doctoral Dissertation, University of Washington. Accessed April 13, 2021. http://hdl.handle.net/1773/43098.

MLA Handbook (7th Edition):

Warrier, Rahul Balakrishna. “Inferring human intent in novice human-in-the-loop control tasks.” 2018. Web. 13 Apr 2021.

Vancouver:

Warrier RB. Inferring human intent in novice human-in-the-loop control tasks. [Internet] [Doctoral dissertation]. University of Washington; 2018. [cited 2021 Apr 13]. Available from: http://hdl.handle.net/1773/43098.

Council of Science Editors:

Warrier RB. Inferring human intent in novice human-in-the-loop control tasks. [Doctoral Dissertation]. University of Washington; 2018. Available from: http://hdl.handle.net/1773/43098


University of Waterloo

14. Vandenhof, Colin. Asking for Help with a Cost in Reinforcement Learning.

Degree: 2020, University of Waterloo

 Reinforcement learning (RL) is a powerful tool for developing intelligent agents, and the use of neural networks makes RL techniques more scalable to challenging real-world… (more)

Subjects/Keywords: reinforcement learning; apprenticeship learning; imitation learning; learning from demonstration; human-in-the-loop; interactive reinforcement learning; deep reinforcement learning; active learning; Reinforcement learning; Active learning

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APA (6th Edition):

Vandenhof, C. (2020). Asking for Help with a Cost in Reinforcement Learning. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/15872

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Vandenhof, Colin. “Asking for Help with a Cost in Reinforcement Learning.” 2020. Thesis, University of Waterloo. Accessed April 13, 2021. http://hdl.handle.net/10012/15872.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Vandenhof, Colin. “Asking for Help with a Cost in Reinforcement Learning.” 2020. Web. 13 Apr 2021.

Vancouver:

Vandenhof C. Asking for Help with a Cost in Reinforcement Learning. [Internet] [Thesis]. University of Waterloo; 2020. [cited 2021 Apr 13]. Available from: http://hdl.handle.net/10012/15872.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Vandenhof C. Asking for Help with a Cost in Reinforcement Learning. [Thesis]. University of Waterloo; 2020. Available from: http://hdl.handle.net/10012/15872

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Colorado

15. Ravanbakhsh, Hadi. Inductive Certificate Synthesis for Control Design.

Degree: PhD, 2018, University of Colorado

  The focus of this thesis is developing a framework for designing correct-by-construction controllers using control certificates. We use nonlinear dynamical systems to model the… (more)

Subjects/Keywords: control lyapunov function; control synthesis; formal methods; learning from demonstration; learning theory; lyapunov analysis; Computer Sciences; Logic and Foundations; Robotics

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Ravanbakhsh, H. (2018). Inductive Certificate Synthesis for Control Design. (Doctoral Dissertation). University of Colorado. Retrieved from https://scholar.colorado.edu/csci_gradetds/174

Chicago Manual of Style (16th Edition):

Ravanbakhsh, Hadi. “Inductive Certificate Synthesis for Control Design.” 2018. Doctoral Dissertation, University of Colorado. Accessed April 13, 2021. https://scholar.colorado.edu/csci_gradetds/174.

MLA Handbook (7th Edition):

Ravanbakhsh, Hadi. “Inductive Certificate Synthesis for Control Design.” 2018. Web. 13 Apr 2021.

Vancouver:

Ravanbakhsh H. Inductive Certificate Synthesis for Control Design. [Internet] [Doctoral dissertation]. University of Colorado; 2018. [cited 2021 Apr 13]. Available from: https://scholar.colorado.edu/csci_gradetds/174.

Council of Science Editors:

Ravanbakhsh H. Inductive Certificate Synthesis for Control Design. [Doctoral Dissertation]. University of Colorado; 2018. Available from: https://scholar.colorado.edu/csci_gradetds/174

16. Ehlers, Dennis. Learning Search Strategies from Human Demonstration for Robotic Assembly Tasks.

Degree: Space Technology, 2018, Luleå University of Technology

Learning from Demonstration (LfD) has been used in robotics research for the last decades to solve issues pertaining to conventional programming of robots. This… (more)

Subjects/Keywords: learning from demonstration; robotics; robotic assembly; search strategies; learning search; compliant motion; Aerospace Engineering; Rymd- och flygteknik

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Ehlers, D. (2018). Learning Search Strategies from Human Demonstration for Robotic Assembly Tasks. (Thesis). Luleå University of Technology. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-72052

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Ehlers, Dennis. “Learning Search Strategies from Human Demonstration for Robotic Assembly Tasks.” 2018. Thesis, Luleå University of Technology. Accessed April 13, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-72052.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Ehlers, Dennis. “Learning Search Strategies from Human Demonstration for Robotic Assembly Tasks.” 2018. Web. 13 Apr 2021.

Vancouver:

Ehlers D. Learning Search Strategies from Human Demonstration for Robotic Assembly Tasks. [Internet] [Thesis]. Luleå University of Technology; 2018. [cited 2021 Apr 13]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-72052.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Ehlers D. Learning Search Strategies from Human Demonstration for Robotic Assembly Tasks. [Thesis]. Luleå University of Technology; 2018. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-72052

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

17. Koskinopoulou, Maria. Learning from demonstration to accomplish robotic manipulation tasks.

Degree: 2019, University of Crete (UOC); Πανεπιστήμιο Κρήτης

 The current PhD thesis addresses the formulation and implementation of a methodological framework for robot Learning from Demonstration (LfD). The latter refers to methodologies that… (more)

Subjects/Keywords: Ρομποτική; Robotics; Learning from demonstration; Machine learning; Latent representation; Neural networks; Human-robot interaction; Temporal planning; Force-based manipulation

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Koskinopoulou, M. (2019). Learning from demonstration to accomplish robotic manipulation tasks. (Thesis). University of Crete (UOC); Πανεπιστήμιο Κρήτης. Retrieved from http://hdl.handle.net/10442/hedi/47241

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Koskinopoulou, Maria. “Learning from demonstration to accomplish robotic manipulation tasks.” 2019. Thesis, University of Crete (UOC); Πανεπιστήμιο Κρήτης. Accessed April 13, 2021. http://hdl.handle.net/10442/hedi/47241.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Koskinopoulou, Maria. “Learning from demonstration to accomplish robotic manipulation tasks.” 2019. Web. 13 Apr 2021.

Vancouver:

Koskinopoulou M. Learning from demonstration to accomplish robotic manipulation tasks. [Internet] [Thesis]. University of Crete (UOC); Πανεπιστήμιο Κρήτης; 2019. [cited 2021 Apr 13]. Available from: http://hdl.handle.net/10442/hedi/47241.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Koskinopoulou M. Learning from demonstration to accomplish robotic manipulation tasks. [Thesis]. University of Crete (UOC); Πανεπιστήμιο Κρήτης; 2019. Available from: http://hdl.handle.net/10442/hedi/47241

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

18. Pereira, Renato de Pontes. HIGMN : an IGMN-based hierarchical architecture and its applications for robotic tasks.

Degree: 2013, Brazil

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… (more)

Subjects/Keywords: Inteligência artificial; Redes neurais; Robótica; Hierarchical incremental Gaussian mixture network; IGMN; Robotics; Learning from demonstration; Deep learning

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Pereira, R. d. P. (2013). HIGMN : an IGMN-based hierarchical architecture and its applications for robotic tasks. (Masters Thesis). Brazil. Retrieved from http://hdl.handle.net/10183/80752

Chicago Manual of Style (16th Edition):

Pereira, Renato de Pontes. “HIGMN : an IGMN-based hierarchical architecture and its applications for robotic tasks.” 2013. Masters Thesis, Brazil. Accessed April 13, 2021. http://hdl.handle.net/10183/80752.

MLA Handbook (7th Edition):

Pereira, Renato de Pontes. “HIGMN : an IGMN-based hierarchical architecture and its applications for robotic tasks.” 2013. Web. 13 Apr 2021.

Vancouver:

Pereira RdP. HIGMN : an IGMN-based hierarchical architecture and its applications for robotic tasks. [Internet] [Masters thesis]. Brazil; 2013. [cited 2021 Apr 13]. Available from: http://hdl.handle.net/10183/80752.

Council of Science Editors:

Pereira RdP. HIGMN : an IGMN-based hierarchical architecture and its applications for robotic tasks. [Masters Thesis]. Brazil; 2013. Available from: http://hdl.handle.net/10183/80752


KTH

19. Yadav, Mayank. Learning Robotic Reactive Behaviour from Demonstration via Dynamic Tree.

Degree: Electrical Engineering and Computer Science (EECS), 2020, KTH

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… (more)

Subjects/Keywords: Learning from Demonstration; Sensor-motor coupling; Reactive tree; Behaviour hierarchy; Robotics; Lärande av demonstration; sensor-motorkoppling; reaktivt träd; beteendeshierarki; robotik; Computer and Information Sciences; Data- och informationsvetenskap

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Yadav, M. (2020). Learning Robotic Reactive Behaviour from Demonstration via Dynamic Tree. (Thesis). KTH. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-285563

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Yadav, Mayank. “Learning Robotic Reactive Behaviour from Demonstration via Dynamic Tree.” 2020. Thesis, KTH. Accessed April 13, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-285563.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Yadav, Mayank. “Learning Robotic Reactive Behaviour from Demonstration via Dynamic Tree.” 2020. Web. 13 Apr 2021.

Vancouver:

Yadav M. Learning Robotic Reactive Behaviour from Demonstration via Dynamic Tree. [Internet] [Thesis]. KTH; 2020. [cited 2021 Apr 13]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-285563.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Yadav M. Learning Robotic Reactive Behaviour from Demonstration via Dynamic Tree. [Thesis]. KTH; 2020. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-285563

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Loughborough University

20. Zhao, Yuchen. Human skill capturing and modelling using wearable devices.

Degree: PhD, 2017, Loughborough University

 Industrial robots are delivering more and more manipulation services in manufacturing. However, when the task is complex, it is difficult to programme a robot to… (more)

Subjects/Keywords: 670.42; Manufacturing automation; Force based control; Motion Capturing (MoCap); Learning from Demonstration (LfD); Surface electromyography (sEMG); Robots

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Zhao, Y. (2017). Human skill capturing and modelling using wearable devices. (Doctoral Dissertation). Loughborough University. Retrieved from http://hdl.handle.net/2134/27613

Chicago Manual of Style (16th Edition):

Zhao, Yuchen. “Human skill capturing and modelling using wearable devices.” 2017. Doctoral Dissertation, Loughborough University. Accessed April 13, 2021. http://hdl.handle.net/2134/27613.

MLA Handbook (7th Edition):

Zhao, Yuchen. “Human skill capturing and modelling using wearable devices.” 2017. Web. 13 Apr 2021.

Vancouver:

Zhao Y. Human skill capturing and modelling using wearable devices. [Internet] [Doctoral dissertation]. Loughborough University; 2017. [cited 2021 Apr 13]. Available from: http://hdl.handle.net/2134/27613.

Council of Science Editors:

Zhao Y. Human skill capturing and modelling using wearable devices. [Doctoral Dissertation]. Loughborough University; 2017. Available from: http://hdl.handle.net/2134/27613


University of Southern California

21. Feil-Seifer, David J. Data-driven interaction methods for socially assistive robotics: validation with children with autism spectrum disorders.

Degree: PhD, Computer Science, 2012, University of Southern California

 There exists a great untapped potential for the use of intelligent robots as therapeutic social partners for children. However, enabling a robot to understand social… (more)

Subjects/Keywords: human-robot interaction; autism; socially assistive robotics; behavior modeling; learning from demonstration; imitation; SAR; HRI; ASD

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APA (6th Edition):

Feil-Seifer, D. J. (2012). Data-driven interaction methods for socially assistive robotics: validation with children with autism spectrum disorders. (Doctoral Dissertation). University of Southern California. Retrieved from http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/217625/rec/1775

Chicago Manual of Style (16th Edition):

Feil-Seifer, David J. “Data-driven interaction methods for socially assistive robotics: validation with children with autism spectrum disorders.” 2012. Doctoral Dissertation, University of Southern California. Accessed April 13, 2021. http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/217625/rec/1775.

MLA Handbook (7th Edition):

Feil-Seifer, David J. “Data-driven interaction methods for socially assistive robotics: validation with children with autism spectrum disorders.” 2012. Web. 13 Apr 2021.

Vancouver:

Feil-Seifer DJ. Data-driven interaction methods for socially assistive robotics: validation with children with autism spectrum disorders. [Internet] [Doctoral dissertation]. University of Southern California; 2012. [cited 2021 Apr 13]. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/217625/rec/1775.

Council of Science Editors:

Feil-Seifer DJ. Data-driven interaction methods for socially assistive robotics: validation with children with autism spectrum disorders. [Doctoral Dissertation]. University of Southern California; 2012. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/217625/rec/1775

22. Shin, Ku Jin. Nonprehensile Manipulation via Multisensory Learning from Demonstration.

Degree: 2018, University of Waterloo

 Dexterous manipulation problem concerns control of a robot hand to manipulate an object in a desired manner. While classical dexterous manipulation strategies are based on… (more)

Subjects/Keywords: Robotics; Dexterous Manipulation; Learning from Demonstration

…we employ recent techniques of learning from demonstration (LfD) for dexterous… …description of the models of the hand and the object. 1.2.2 Learning from Demonstration (LfD… …x29; Learning from Demonstration (LfD) gained attention in the field of… …learning a specific task or action from demonstration data acquired from various sensors. Terms… …tools for learning from demonstration (LfD) framework and robot manipulator model… 

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APA (6th Edition):

Shin, K. J. (2018). Nonprehensile Manipulation via Multisensory Learning from Demonstration. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/14195

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Shin, Ku Jin. “Nonprehensile Manipulation via Multisensory Learning from Demonstration.” 2018. Thesis, University of Waterloo. Accessed April 13, 2021. http://hdl.handle.net/10012/14195.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Shin, Ku Jin. “Nonprehensile Manipulation via Multisensory Learning from Demonstration.” 2018. Web. 13 Apr 2021.

Vancouver:

Shin KJ. Nonprehensile Manipulation via Multisensory Learning from Demonstration. [Internet] [Thesis]. University of Waterloo; 2018. [cited 2021 Apr 13]. Available from: http://hdl.handle.net/10012/14195.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Shin KJ. Nonprehensile Manipulation via Multisensory Learning from Demonstration. [Thesis]. University of Waterloo; 2018. Available from: http://hdl.handle.net/10012/14195

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Pennsylvania

23. Wen, Min. Reinforcement Learning With High-Level Task Specifications.

Degree: 2019, University of Pennsylvania

 Reinforcement learning (RL) has been widely used, for example, in robotics, recommendation systems, and financial services. Existing RL algorithms typically optimize reward-based surrogates rather than… (more)

Subjects/Keywords: Game theory; Inverse reinforcement learning; Learning-based control; Learning from demonstration; Reinforcement learning; Temporal logic specifications; Artificial Intelligence and Robotics; Computer Sciences

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Wen, M. (2019). Reinforcement Learning With High-Level Task Specifications. (Thesis). University of Pennsylvania. Retrieved from https://repository.upenn.edu/edissertations/3509

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Wen, Min. “Reinforcement Learning With High-Level Task Specifications.” 2019. Thesis, University of Pennsylvania. Accessed April 13, 2021. https://repository.upenn.edu/edissertations/3509.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Wen, Min. “Reinforcement Learning With High-Level Task Specifications.” 2019. Web. 13 Apr 2021.

Vancouver:

Wen M. Reinforcement Learning With High-Level Task Specifications. [Internet] [Thesis]. University of Pennsylvania; 2019. [cited 2021 Apr 13]. Available from: https://repository.upenn.edu/edissertations/3509.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Wen M. Reinforcement Learning With High-Level Task Specifications. [Thesis]. University of Pennsylvania; 2019. Available from: https://repository.upenn.edu/edissertations/3509

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Carnegie Mellon University

24. Silver, David. Learning Preference Models for Autonomous Mobile Robots in Complex Domains.

Degree: 2010, Carnegie Mellon University

 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… (more)

Subjects/Keywords: Mobile Robots; Field Robotics; Learning from Demonstration; Imitation Learning; Inverse Optimal Control; Active Learning; Preference Models; Cost Functions; Parameter Tuning; Artificial Intelligence and Robotics

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APA (6th Edition):

Silver, D. (2010). Learning Preference Models for Autonomous Mobile Robots in Complex Domains. (Thesis). Carnegie Mellon University. Retrieved from http://repository.cmu.edu/dissertations/551

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Silver, David. “Learning Preference Models for Autonomous Mobile Robots in Complex Domains.” 2010. Thesis, Carnegie Mellon University. Accessed April 13, 2021. http://repository.cmu.edu/dissertations/551.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Silver, David. “Learning Preference Models for Autonomous Mobile Robots in Complex Domains.” 2010. Web. 13 Apr 2021.

Vancouver:

Silver D. Learning Preference Models for Autonomous Mobile Robots in Complex Domains. [Internet] [Thesis]. Carnegie Mellon University; 2010. [cited 2021 Apr 13]. Available from: http://repository.cmu.edu/dissertations/551.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Silver D. Learning Preference Models for Autonomous Mobile Robots in Complex Domains. [Thesis]. Carnegie Mellon University; 2010. Available from: http://repository.cmu.edu/dissertations/551

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

25. Kumra, Sulabh. Robot Learning Dual-Arm Manipulation Tasks by Trial-and-Error and Multiple Human Demonstrations.

Degree: MS, Electrical Engineering, 2015, Rochester Institute of Technology

  In robotics, there is a need of an interactive and expedite learning method as experience is expensive. In this research, we propose two different… (more)

Subjects/Keywords: Baxter learns; Demonstration; Q learning; Robot learning; Robot learning from demonstration

…field of robot learning from demonstration (RLfD). The chapter also discusses… …section, concept of robot learning from demonstration and recent work in this field is discussed… …2.2 Robot Learning from Demonstration The presence of robots in society has become even more… …Robot Learning from Demonstration (RLfD) [26] is a technique to enable a… …Learning from Demonstration started in 1980s. However, until date most of the robots are… 

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Kumra, S. (2015). Robot Learning Dual-Arm Manipulation Tasks by Trial-and-Error and Multiple Human Demonstrations. (Masters Thesis). Rochester Institute of Technology. Retrieved from https://scholarworks.rit.edu/theses/8755

Chicago Manual of Style (16th Edition):

Kumra, Sulabh. “Robot Learning Dual-Arm Manipulation Tasks by Trial-and-Error and Multiple Human Demonstrations.” 2015. Masters Thesis, Rochester Institute of Technology. Accessed April 13, 2021. https://scholarworks.rit.edu/theses/8755.

MLA Handbook (7th Edition):

Kumra, Sulabh. “Robot Learning Dual-Arm Manipulation Tasks by Trial-and-Error and Multiple Human Demonstrations.” 2015. Web. 13 Apr 2021.

Vancouver:

Kumra S. Robot Learning Dual-Arm Manipulation Tasks by Trial-and-Error and Multiple Human Demonstrations. [Internet] [Masters thesis]. Rochester Institute of Technology; 2015. [cited 2021 Apr 13]. Available from: https://scholarworks.rit.edu/theses/8755.

Council of Science Editors:

Kumra S. Robot Learning Dual-Arm Manipulation Tasks by Trial-and-Error and Multiple Human Demonstrations. [Masters Thesis]. Rochester Institute of Technology; 2015. Available from: https://scholarworks.rit.edu/theses/8755

26. Narayanan, Krishna Kumar. Learning vision based mobile robot behaviors from demonstration.

Degree: 2015, Technische Universität Dortmund

Subjects/Keywords: Mobile robotics; Learning from demonstration; Behavior based robotics; Visual navigation; 620

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Narayanan, K. K. (2015). Learning vision based mobile robot behaviors from demonstration. (Doctoral Dissertation). Technische Universität Dortmund. Retrieved from http://dx.doi.org/10.17877/DE290R-16570

Chicago Manual of Style (16th Edition):

Narayanan, Krishna Kumar. “Learning vision based mobile robot behaviors from demonstration.” 2015. Doctoral Dissertation, Technische Universität Dortmund. Accessed April 13, 2021. http://dx.doi.org/10.17877/DE290R-16570.

MLA Handbook (7th Edition):

Narayanan, Krishna Kumar. “Learning vision based mobile robot behaviors from demonstration.” 2015. Web. 13 Apr 2021.

Vancouver:

Narayanan KK. Learning vision based mobile robot behaviors from demonstration. [Internet] [Doctoral dissertation]. Technische Universität Dortmund; 2015. [cited 2021 Apr 13]. Available from: http://dx.doi.org/10.17877/DE290R-16570.

Council of Science Editors:

Narayanan KK. Learning vision based mobile robot behaviors from demonstration. [Doctoral Dissertation]. Technische Universität Dortmund; 2015. Available from: http://dx.doi.org/10.17877/DE290R-16570

27. Lin, Hsien-Chung. Embedding Intelligence into Robotic Systems - Programming, Learning, and Planning.

Degree: Mechanical Engineering, 2018, University of California – Berkeley

 Although robots play increasingly important roles in automated production due to their high efficiency, high accuracy, and high repeatability, new challenges for robots arise from(more)

Subjects/Keywords: Robotics; Human-Robot Interaction; Learning from Demonstration; Motion Planning; Robotics

…part follows the framework of learning from demonstration and studies the skill learning of… …learning from demonstration with remote lead through teaching, 4) robot grasp transferring… …process, the learning from demonstration (LfD) is proposed to transfer the human… …idea to “Learning from Demonstration” (LfD), which is not just a record and play… …Programming is to retrieve the information/knowledge from humans. Learning is to generalize the… 

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Lin, H. (2018). Embedding Intelligence into Robotic Systems - Programming, Learning, and Planning. (Thesis). University of California – Berkeley. Retrieved from http://www.escholarship.org/uc/item/5z62k45g

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Lin, Hsien-Chung. “Embedding Intelligence into Robotic Systems - Programming, Learning, and Planning.” 2018. Thesis, University of California – Berkeley. Accessed April 13, 2021. http://www.escholarship.org/uc/item/5z62k45g.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Lin, Hsien-Chung. “Embedding Intelligence into Robotic Systems - Programming, Learning, and Planning.” 2018. Web. 13 Apr 2021.

Vancouver:

Lin H. Embedding Intelligence into Robotic Systems - Programming, Learning, and Planning. [Internet] [Thesis]. University of California – Berkeley; 2018. [cited 2021 Apr 13]. Available from: http://www.escholarship.org/uc/item/5z62k45g.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Lin H. Embedding Intelligence into Robotic Systems - Programming, Learning, and Planning. [Thesis]. University of California – Berkeley; 2018. Available from: http://www.escholarship.org/uc/item/5z62k45g

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

28. Niekum, Scott D. Semantically Grounded Learning from Unstructured Demonstrations.

Degree: PhD, Computer Science, 2013, U of Massachusetts : PhD

  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… (more)

Subjects/Keywords: Bayesian nonparametrics; Learning from demonstration; PR2; Robotics; Computer Sciences; Robotics

…expert. For this reason, learning from demonstration (LfD) has become a popular… …6 2.1.1 2.1.2 2.1.3 2.2 2.3 Learning from Demonstration… …Overview of the iterative learning from demonstration framework. . . . . . . 85 5.2 A… …recent years, learning from demonstration (LfD) [4] has become a popular… …learning from demonstration. Prior work and alternate approaches to LfD, subgoal identification… 

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Niekum, S. D. (2013). Semantically Grounded Learning from Unstructured Demonstrations. (Doctoral Dissertation). U of Massachusetts : PhD. Retrieved from https://scholarworks.umass.edu/open_access_dissertations/811

Chicago Manual of Style (16th Edition):

Niekum, Scott D. “Semantically Grounded Learning from Unstructured Demonstrations.” 2013. Doctoral Dissertation, U of Massachusetts : PhD. Accessed April 13, 2021. https://scholarworks.umass.edu/open_access_dissertations/811.

MLA Handbook (7th Edition):

Niekum, Scott D. “Semantically Grounded Learning from Unstructured Demonstrations.” 2013. Web. 13 Apr 2021.

Vancouver:

Niekum SD. Semantically Grounded Learning from Unstructured Demonstrations. [Internet] [Doctoral dissertation]. U of Massachusetts : PhD; 2013. [cited 2021 Apr 13]. Available from: https://scholarworks.umass.edu/open_access_dissertations/811.

Council of Science Editors:

Niekum SD. Semantically Grounded Learning from Unstructured Demonstrations. [Doctoral Dissertation]. U of Massachusetts : PhD; 2013. Available from: https://scholarworks.umass.edu/open_access_dissertations/811

29. Johnson, Miles. Inverse optimal control for deterministic continuous-time nonlinear systems.

Degree: PhD, 4048, 2014, University of Illinois – Urbana-Champaign

 Inverse optimal control is the problem of computing a cost function with respect to which observed state input trajectories are optimal. We present a new… (more)

Subjects/Keywords: optimal control; inverse reinforcement learning; inverse optimal control; apprenticeship learning; Learning from demonstration; iterative learning control

…56 This figure shows an outline of our quadrotor learning from demonstration method… …control policy resulting from our learning from demonstration method. This figure shows the… …more general problem of learning from demonstration. This problem is often referred to as… …imitation learning or apprenticeship learning. The problem of learning from demonstration is to… …demonstration methods are applied in three different areas. First, learning from demonstration has… 

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APA (6th Edition):

Johnson, M. (2014). Inverse optimal control for deterministic continuous-time nonlinear systems. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/46747

Chicago Manual of Style (16th Edition):

Johnson, Miles. “Inverse optimal control for deterministic continuous-time nonlinear systems.” 2014. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed April 13, 2021. http://hdl.handle.net/2142/46747.

MLA Handbook (7th Edition):

Johnson, Miles. “Inverse optimal control for deterministic continuous-time nonlinear systems.” 2014. Web. 13 Apr 2021.

Vancouver:

Johnson M. Inverse optimal control for deterministic continuous-time nonlinear systems. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2014. [cited 2021 Apr 13]. Available from: http://hdl.handle.net/2142/46747.

Council of Science Editors:

Johnson M. Inverse optimal control for deterministic continuous-time nonlinear systems. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2014. Available from: http://hdl.handle.net/2142/46747

30. Xia, Chen. Apprentissage Intelligent des Robots Mobiles dans la Navigation Autonome : Intelligent Mobile Robot Learning in Autonomous Navigation.

Degree: Docteur es, Automatique, génie informatique, traitement du signal et des images, 2015, Ecole centrale de Lille

Les robots modernes sont appelés à effectuer des opérations ou tâches complexes et la capacité de navigation autonome dans un environnement dynamique est un besoin… (more)

Subjects/Keywords: Apprentissage automatique; Apprentissage par renforcement; Réseau de neurones; Navigation autonome; Robots mobiles; Apprentissage par démonstrations; Processus de décision markovien; Machine learning; Reinforcement learning; Neural network; Autonomous navigation; Mobile robots; Learning from demonstration; Markov decision processes

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Xia, C. (2015). Apprentissage Intelligent des Robots Mobiles dans la Navigation Autonome : Intelligent Mobile Robot Learning in Autonomous Navigation. (Doctoral Dissertation). Ecole centrale de Lille. Retrieved from http://www.theses.fr/2015ECLI0026

Chicago Manual of Style (16th Edition):

Xia, Chen. “Apprentissage Intelligent des Robots Mobiles dans la Navigation Autonome : Intelligent Mobile Robot Learning in Autonomous Navigation.” 2015. Doctoral Dissertation, Ecole centrale de Lille. Accessed April 13, 2021. http://www.theses.fr/2015ECLI0026.

MLA Handbook (7th Edition):

Xia, Chen. “Apprentissage Intelligent des Robots Mobiles dans la Navigation Autonome : Intelligent Mobile Robot Learning in Autonomous Navigation.” 2015. Web. 13 Apr 2021.

Vancouver:

Xia C. Apprentissage Intelligent des Robots Mobiles dans la Navigation Autonome : Intelligent Mobile Robot Learning in Autonomous Navigation. [Internet] [Doctoral dissertation]. Ecole centrale de Lille; 2015. [cited 2021 Apr 13]. Available from: http://www.theses.fr/2015ECLI0026.

Council of Science Editors:

Xia C. Apprentissage Intelligent des Robots Mobiles dans la Navigation Autonome : Intelligent Mobile Robot Learning in Autonomous Navigation. [Doctoral Dissertation]. Ecole centrale de Lille; 2015. Available from: http://www.theses.fr/2015ECLI0026

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