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You searched for subject:(Learning from Demonstrations). Showing records 1 – 2 of 2 total matches.

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1. Carpio Mazariegos, Estuardo Rene. Learning Temporal Dynamics of Human-Robot Interactions from Demonstrations.

Degree: MS, 2018, University of New Hampshire

The presence of robots in society is becoming increasingly common, triggering the need to learn reliable policies to automate human-robot interactions (HRI). Manually developing policies for HRI is particularly challenging due to the complexity introduced by the human component. The aim of this thesis is to explore the benefits of leveraging temporal reasoning to learn policies for HRIs from demonstrations. This thesis proposes and evaluates two distinct temporal reasoning approaches. The first one consists of a temporal-reasoning-based learning from demonstration (TR-LfD) framework that employs a variant of an Interval Temporal Bayesian Network to learn the temporal dynamics of an interaction. TR-LfD exploits Allen’s interval algebra (IA) and Bayesian networks to effectively learn complex temporal structures. The second approach consists of a novel temporal reasoning model, the Temporal Context Graph (TCG). TCGs combine IA, n-grams models, and directed graphs to model interactions with cyclical atomic actions and temporal structures with sequential and parallel relationships. The proposed temporal reasoning models are evaluated using two experiments consisting of autonomous robot-mediated behavioral interventions. Results indicate that leveraging temporal reasoning can improve policy generation and execution in LfD frameworks. Specifically, these models can be used to limit the action space of a robot during an interaction, thus simplifying policy selection and effectively addressing the issue of perceptual aliasing. Advisors/Committee Members: Momotaz Begum, Philip J Hatcher, Marek Petrik.

Subjects/Keywords: HRI; human-robot interaction; learning from demonstrations; LfD; temporal reasoning

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

APA (6th Edition):

Carpio Mazariegos, E. R. (2018). Learning Temporal Dynamics of Human-Robot Interactions from Demonstrations. (Thesis). University of New Hampshire. Retrieved from https://scholars.unh.edu/thesis/1237

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):

Carpio Mazariegos, Estuardo Rene. “Learning Temporal Dynamics of Human-Robot Interactions from Demonstrations.” 2018. Thesis, University of New Hampshire. Accessed March 29, 2020. https://scholars.unh.edu/thesis/1237.

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

MLA Handbook (7th Edition):

Carpio Mazariegos, Estuardo Rene. “Learning Temporal Dynamics of Human-Robot Interactions from Demonstrations.” 2018. Web. 29 Mar 2020.

Vancouver:

Carpio Mazariegos ER. Learning Temporal Dynamics of Human-Robot Interactions from Demonstrations. [Internet] [Thesis]. University of New Hampshire; 2018. [cited 2020 Mar 29]. Available from: https://scholars.unh.edu/thesis/1237.

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

Council of Science Editors:

Carpio Mazariegos ER. Learning Temporal Dynamics of Human-Robot Interactions from Demonstrations. [Thesis]. University of New Hampshire; 2018. Available from: https://scholars.unh.edu/thesis/1237

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

2. YUAN JINQIANG. LEARNING ACTIONS FROM DEMONSTRATIONS FOR MANIPULATION TASK PLANNING.

Degree: 2019, National University of Singapore

Subjects/Keywords: Learning from Demonstrations; Combined Task and Motion Planning; Manipulationg Planning; Robotic Learning; Dynamic Movement Primitives; Task Planning

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

APA (6th Edition):

JINQIANG, Y. (2019). LEARNING ACTIONS FROM DEMONSTRATIONS FOR MANIPULATION TASK PLANNING. (Thesis). National University of Singapore. Retrieved from https://scholarbank.nus.edu.sg/handle/10635/158088

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):

JINQIANG, YUAN. “LEARNING ACTIONS FROM DEMONSTRATIONS FOR MANIPULATION TASK PLANNING.” 2019. Thesis, National University of Singapore. Accessed March 29, 2020. https://scholarbank.nus.edu.sg/handle/10635/158088.

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

MLA Handbook (7th Edition):

JINQIANG, YUAN. “LEARNING ACTIONS FROM DEMONSTRATIONS FOR MANIPULATION TASK PLANNING.” 2019. Web. 29 Mar 2020.

Vancouver:

JINQIANG Y. LEARNING ACTIONS FROM DEMONSTRATIONS FOR MANIPULATION TASK PLANNING. [Internet] [Thesis]. National University of Singapore; 2019. [cited 2020 Mar 29]. Available from: https://scholarbank.nus.edu.sg/handle/10635/158088.

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

Council of Science Editors:

JINQIANG Y. LEARNING ACTIONS FROM DEMONSTRATIONS FOR MANIPULATION TASK PLANNING. [Thesis]. National University of Singapore; 2019. Available from: https://scholarbank.nus.edu.sg/handle/10635/158088

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

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