Advanced search options

Advanced Search Options 🞨

Browse by author name (“Author name starts with…”).

Find ETDs with:

in
/  
in
/  
in
/  
in

Written in Published in Earliest date Latest date

Sorted by

Results per page:

Sorted by: relevance · author · university · dateNew search

You searched for +publisher:"Georgia Tech" +contributor:("Chernova, Sonia"). Showing records 1 – 20 of 20 total matches.

Search Limiters

Last 2 Years | English Only

No search limiters apply to these results.

▼ Search Limiters


Georgia Tech

1. Okamoto, Kazuhide. Optimal covariance steering: Theory and its application to autonomous driving.

Degree: PhD, Aerospace Engineering, 2019, Georgia Tech

 Optimal control under uncertainty has been one of the central research topics in the control community for decades. While a number of theories have been… (more)

Subjects/Keywords: Stochastic control; Optimal control; Model predictive control; Vehicle path planning

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Okamoto, K. (2019). Optimal covariance steering: Theory and its application to autonomous driving. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/62260

Chicago Manual of Style (16th Edition):

Okamoto, Kazuhide. “Optimal covariance steering: Theory and its application to autonomous driving.” 2019. Doctoral Dissertation, Georgia Tech. Accessed April 21, 2021. http://hdl.handle.net/1853/62260.

MLA Handbook (7th Edition):

Okamoto, Kazuhide. “Optimal covariance steering: Theory and its application to autonomous driving.” 2019. Web. 21 Apr 2021.

Vancouver:

Okamoto K. Optimal covariance steering: Theory and its application to autonomous driving. [Internet] [Doctoral dissertation]. Georgia Tech; 2019. [cited 2021 Apr 21]. Available from: http://hdl.handle.net/1853/62260.

Council of Science Editors:

Okamoto K. Optimal covariance steering: Theory and its application to autonomous driving. [Doctoral Dissertation]. Georgia Tech; 2019. Available from: http://hdl.handle.net/1853/62260


Georgia Tech

2. Bullard, Kalesha. Managing learning interactions for collaborative robot learning.

Degree: PhD, Interactive Computing, 2019, Georgia Tech

 Robotic assistants should be able to actively engage their human partner(s) to generalize knowledge about relevant tasks within their shared environment. Yet a key challenge… (more)

Subjects/Keywords: Interactive robot learning; Active learning

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Bullard, K. (2019). Managing learning interactions for collaborative robot learning. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/62294

Chicago Manual of Style (16th Edition):

Bullard, Kalesha. “Managing learning interactions for collaborative robot learning.” 2019. Doctoral Dissertation, Georgia Tech. Accessed April 21, 2021. http://hdl.handle.net/1853/62294.

MLA Handbook (7th Edition):

Bullard, Kalesha. “Managing learning interactions for collaborative robot learning.” 2019. Web. 21 Apr 2021.

Vancouver:

Bullard K. Managing learning interactions for collaborative robot learning. [Internet] [Doctoral dissertation]. Georgia Tech; 2019. [cited 2021 Apr 21]. Available from: http://hdl.handle.net/1853/62294.

Council of Science Editors:

Bullard K. Managing learning interactions for collaborative robot learning. [Doctoral Dissertation]. Georgia Tech; 2019. Available from: http://hdl.handle.net/1853/62294


Georgia Tech

3. Ma, Mingyue (Lanssie). Furthering human-robot teaming, interaction, and metrics through computational methods and analysis.

Degree: PhD, Aerospace Engineering, 2019, Georgia Tech

 Human-robot teaming is a complex design trade space with dynamic aspects and particulars. In order to support future day human-robot teams and scenarios, we need… (more)

Subjects/Keywords: Human-robot teaming; Human-robot interaction; Work allocation; Modeling; Simulation

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Ma, M. (. (2019). Furthering human-robot teaming, interaction, and metrics through computational methods and analysis. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/62655

Chicago Manual of Style (16th Edition):

Ma, Mingyue (Lanssie). “Furthering human-robot teaming, interaction, and metrics through computational methods and analysis.” 2019. Doctoral Dissertation, Georgia Tech. Accessed April 21, 2021. http://hdl.handle.net/1853/62655.

MLA Handbook (7th Edition):

Ma, Mingyue (Lanssie). “Furthering human-robot teaming, interaction, and metrics through computational methods and analysis.” 2019. Web. 21 Apr 2021.

Vancouver:

Ma M(. Furthering human-robot teaming, interaction, and metrics through computational methods and analysis. [Internet] [Doctoral dissertation]. Georgia Tech; 2019. [cited 2021 Apr 21]. Available from: http://hdl.handle.net/1853/62655.

Council of Science Editors:

Ma M(. Furthering human-robot teaming, interaction, and metrics through computational methods and analysis. [Doctoral Dissertation]. Georgia Tech; 2019. Available from: http://hdl.handle.net/1853/62655


Georgia Tech

4. Schroecker, Yannick Karl Daniel. Manipulating state space distributions for sample-efficient imitation-learning.

Degree: PhD, Interactive Computing, 2020, Georgia Tech

 Imitation learning has emerged as one of the most effective approaches to train agents to act intelligently in unstructured and unknown domains. On its own… (more)

Subjects/Keywords: Imitation learning; Reinforcement learning; Deep learning; Machine learning; Artificial intelligence

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Schroecker, Y. K. D. (2020). Manipulating state space distributions for sample-efficient imitation-learning. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/62755

Chicago Manual of Style (16th Edition):

Schroecker, Yannick Karl Daniel. “Manipulating state space distributions for sample-efficient imitation-learning.” 2020. Doctoral Dissertation, Georgia Tech. Accessed April 21, 2021. http://hdl.handle.net/1853/62755.

MLA Handbook (7th Edition):

Schroecker, Yannick Karl Daniel. “Manipulating state space distributions for sample-efficient imitation-learning.” 2020. Web. 21 Apr 2021.

Vancouver:

Schroecker YKD. Manipulating state space distributions for sample-efficient imitation-learning. [Internet] [Doctoral dissertation]. Georgia Tech; 2020. [cited 2021 Apr 21]. Available from: http://hdl.handle.net/1853/62755.

Council of Science Editors:

Schroecker YKD. Manipulating state space distributions for sample-efficient imitation-learning. [Doctoral Dissertation]. Georgia Tech; 2020. Available from: http://hdl.handle.net/1853/62755


Georgia Tech

5. Krening, Samantha. Humans teaching intelligent agents with verbal instruction.

Degree: PhD, Aerospace Engineering, 2019, Georgia Tech

 The widespread integration of robotics into everyday life requires significant improvement in the underlying machine learning (ML) agents to make them more accessible, customizable, and… (more)

Subjects/Keywords: Robotics; Machine learning; Interactive machine learning; Human-agent interaction; Reinforcement learning; Natural language processing; Human-computer interaction; Human factors; Machine learning verification

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Krening, S. (2019). Humans teaching intelligent agents with verbal instruction. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/61232

Chicago Manual of Style (16th Edition):

Krening, Samantha. “Humans teaching intelligent agents with verbal instruction.” 2019. Doctoral Dissertation, Georgia Tech. Accessed April 21, 2021. http://hdl.handle.net/1853/61232.

MLA Handbook (7th Edition):

Krening, Samantha. “Humans teaching intelligent agents with verbal instruction.” 2019. Web. 21 Apr 2021.

Vancouver:

Krening S. Humans teaching intelligent agents with verbal instruction. [Internet] [Doctoral dissertation]. Georgia Tech; 2019. [cited 2021 Apr 21]. Available from: http://hdl.handle.net/1853/61232.

Council of Science Editors:

Krening S. Humans teaching intelligent agents with verbal instruction. [Doctoral Dissertation]. Georgia Tech; 2019. Available from: http://hdl.handle.net/1853/61232


Georgia Tech

6. Edwards, Ashley Deloris. Emulation and imitation via perceptual goal specifications.

Degree: PhD, Computer Science, 2019, Georgia Tech

 This dissertation aims to demonstrate how perceptual goal specifications may be used as alternative representations for specifying domain-specific reward functions for reinforcement learning. The works… (more)

Subjects/Keywords: Reinforcement learning; Goal specification; Imitation learning; Reward design

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Edwards, A. D. (2019). Emulation and imitation via perceptual goal specifications. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/61234

Chicago Manual of Style (16th Edition):

Edwards, Ashley Deloris. “Emulation and imitation via perceptual goal specifications.” 2019. Doctoral Dissertation, Georgia Tech. Accessed April 21, 2021. http://hdl.handle.net/1853/61234.

MLA Handbook (7th Edition):

Edwards, Ashley Deloris. “Emulation and imitation via perceptual goal specifications.” 2019. Web. 21 Apr 2021.

Vancouver:

Edwards AD. Emulation and imitation via perceptual goal specifications. [Internet] [Doctoral dissertation]. Georgia Tech; 2019. [cited 2021 Apr 21]. Available from: http://hdl.handle.net/1853/61234.

Council of Science Editors:

Edwards AD. Emulation and imitation via perceptual goal specifications. [Doctoral Dissertation]. Georgia Tech; 2019. Available from: http://hdl.handle.net/1853/61234


Georgia Tech

7. Pettinati, Michael. Supporting healthy dyadic human relationships with power differentials using robots.

Degree: PhD, Computer Science, 2020, Georgia Tech

 Conflict is a natural part of ever-evolving human-human relationships. The way in which conflicts are handled can result in relationship growth, dissatisfaction (for one or… (more)

Subjects/Keywords: Human-human-robot interaction; Human-relationship modeling; Relationship-focused robotic system

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Pettinati, M. (2020). Supporting healthy dyadic human relationships with power differentials using robots. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/62835

Chicago Manual of Style (16th Edition):

Pettinati, Michael. “Supporting healthy dyadic human relationships with power differentials using robots.” 2020. Doctoral Dissertation, Georgia Tech. Accessed April 21, 2021. http://hdl.handle.net/1853/62835.

MLA Handbook (7th Edition):

Pettinati, Michael. “Supporting healthy dyadic human relationships with power differentials using robots.” 2020. Web. 21 Apr 2021.

Vancouver:

Pettinati M. Supporting healthy dyadic human relationships with power differentials using robots. [Internet] [Doctoral dissertation]. Georgia Tech; 2020. [cited 2021 Apr 21]. Available from: http://hdl.handle.net/1853/62835.

Council of Science Editors:

Pettinati M. Supporting healthy dyadic human relationships with power differentials using robots. [Doctoral Dissertation]. Georgia Tech; 2020. Available from: http://hdl.handle.net/1853/62835


Georgia Tech

8. Chandrasekaran, Arjun. Towards natural human-AI interactions in vision and language.

Degree: PhD, Interactive Computing, 2019, Georgia Tech

 Inter-human interaction is a rich form of communication. Human interactions typically leverage a good theory of mind, involve pragmatics, story-telling, humor, sarcasm, empathy, sympathy, etc.… (more)

Subjects/Keywords: AI; Neural networks; Human-AI interaction; Human-AI collaboration; Humor; Narrative; Sorytelling; Explainable AI; Interpretability; Predictability; Guesswhich; Human-in-loop evaluation

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Chandrasekaran, A. (2019). Towards natural human-AI interactions in vision and language. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/62323

Chicago Manual of Style (16th Edition):

Chandrasekaran, Arjun. “Towards natural human-AI interactions in vision and language.” 2019. Doctoral Dissertation, Georgia Tech. Accessed April 21, 2021. http://hdl.handle.net/1853/62323.

MLA Handbook (7th Edition):

Chandrasekaran, Arjun. “Towards natural human-AI interactions in vision and language.” 2019. Web. 21 Apr 2021.

Vancouver:

Chandrasekaran A. Towards natural human-AI interactions in vision and language. [Internet] [Doctoral dissertation]. Georgia Tech; 2019. [cited 2021 Apr 21]. Available from: http://hdl.handle.net/1853/62323.

Council of Science Editors:

Chandrasekaran A. Towards natural human-AI interactions in vision and language. [Doctoral Dissertation]. Georgia Tech; 2019. Available from: http://hdl.handle.net/1853/62323


Georgia Tech

9. Yan, Xinyan. Efficient trajectory and policy optimization using dynamics models.

Degree: PhD, Interactive Computing, 2020, Georgia Tech

 Data-driven approaches hold the promise of creating the next wave of robots that can perform diverse tasks and adapt to unstructured environments. However, gathering data… (more)

Subjects/Keywords: Trajectory optimization; Policy optimization; State estimation; Model predictive control; Online learning; Statistical learning

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Yan, X. (2020). Efficient trajectory and policy optimization using dynamics models. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/63621

Chicago Manual of Style (16th Edition):

Yan, Xinyan. “Efficient trajectory and policy optimization using dynamics models.” 2020. Doctoral Dissertation, Georgia Tech. Accessed April 21, 2021. http://hdl.handle.net/1853/63621.

MLA Handbook (7th Edition):

Yan, Xinyan. “Efficient trajectory and policy optimization using dynamics models.” 2020. Web. 21 Apr 2021.

Vancouver:

Yan X. Efficient trajectory and policy optimization using dynamics models. [Internet] [Doctoral dissertation]. Georgia Tech; 2020. [cited 2021 Apr 21]. Available from: http://hdl.handle.net/1853/63621.

Council of Science Editors:

Yan X. Efficient trajectory and policy optimization using dynamics models. [Doctoral Dissertation]. Georgia Tech; 2020. Available from: http://hdl.handle.net/1853/63621


Georgia Tech

10. Park, Daehyung. A multimodal execution monitor for assistive robots.

Degree: PhD, Interactive Computing, 2018, Georgia Tech

 Assistive robots have the potential to serve as caregivers, providing assistance with activities of daily living to people with disabilities. Monitoring when something has gone… (more)

Subjects/Keywords: Execution monitor; Assistive robot

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Park, D. (2018). A multimodal execution monitor for assistive robots. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/59860

Chicago Manual of Style (16th Edition):

Park, Daehyung. “A multimodal execution monitor for assistive robots.” 2018. Doctoral Dissertation, Georgia Tech. Accessed April 21, 2021. http://hdl.handle.net/1853/59860.

MLA Handbook (7th Edition):

Park, Daehyung. “A multimodal execution monitor for assistive robots.” 2018. Web. 21 Apr 2021.

Vancouver:

Park D. A multimodal execution monitor for assistive robots. [Internet] [Doctoral dissertation]. Georgia Tech; 2018. [cited 2021 Apr 21]. Available from: http://hdl.handle.net/1853/59860.

Council of Science Editors:

Park D. A multimodal execution monitor for assistive robots. [Doctoral Dissertation]. Georgia Tech; 2018. Available from: http://hdl.handle.net/1853/59860


Georgia Tech

11. Clegg, Alexander William. Modeling Human and Robot Behavior During Dressing Tasks.

Degree: PhD, Interactive Computing, 2020, Georgia Tech

 Human dressing assistance tasks present a multitude of privacy, safety, and independence concerns for the daily lives of a vast number of individuals across the… (more)

Subjects/Keywords: Animation; Cloth; Robot; Physics simulation; Dressing; Deep reinforcement learning; Motion synthesis; Haptics; Activities of daily living; Garment; Control; Modeling capability; Human

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Clegg, A. W. (2020). Modeling Human and Robot Behavior During Dressing Tasks. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/63506

Chicago Manual of Style (16th Edition):

Clegg, Alexander William. “Modeling Human and Robot Behavior During Dressing Tasks.” 2020. Doctoral Dissertation, Georgia Tech. Accessed April 21, 2021. http://hdl.handle.net/1853/63506.

MLA Handbook (7th Edition):

Clegg, Alexander William. “Modeling Human and Robot Behavior During Dressing Tasks.” 2020. Web. 21 Apr 2021.

Vancouver:

Clegg AW. Modeling Human and Robot Behavior During Dressing Tasks. [Internet] [Doctoral dissertation]. Georgia Tech; 2020. [cited 2021 Apr 21]. Available from: http://hdl.handle.net/1853/63506.

Council of Science Editors:

Clegg AW. Modeling Human and Robot Behavior During Dressing Tasks. [Doctoral Dissertation]. Georgia Tech; 2020. Available from: http://hdl.handle.net/1853/63506


Georgia Tech

12. Chu, Fu-Jen. Improving vision-based robotic manipulation with affordance understanding.

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

 The objective of the thesis is to improve robotic manipulation via vision-based affordance understanding, which would advance the application of robotics to industrial use cases,… (more)

Subjects/Keywords: Robotics; Vision; Deep learning; Affordance; Domain adaptation; Grasp; Grasping; Synthetic data

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Chu, F. (2020). Improving vision-based robotic manipulation with affordance understanding. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/63687

Chicago Manual of Style (16th Edition):

Chu, Fu-Jen. “Improving vision-based robotic manipulation with affordance understanding.” 2020. Doctoral Dissertation, Georgia Tech. Accessed April 21, 2021. http://hdl.handle.net/1853/63687.

MLA Handbook (7th Edition):

Chu, Fu-Jen. “Improving vision-based robotic manipulation with affordance understanding.” 2020. Web. 21 Apr 2021.

Vancouver:

Chu F. Improving vision-based robotic manipulation with affordance understanding. [Internet] [Doctoral dissertation]. Georgia Tech; 2020. [cited 2021 Apr 21]. Available from: http://hdl.handle.net/1853/63687.

Council of Science Editors:

Chu F. Improving vision-based robotic manipulation with affordance understanding. [Doctoral Dissertation]. Georgia Tech; 2020. Available from: http://hdl.handle.net/1853/63687


Georgia Tech

13. Fitzgerald, Tesca Kate. Human-guided task transfer for interactive robots.

Degree: PhD, Interactive Computing, 2020, Georgia Tech

 Adaptability is an essential skill in human cognition, enabling us to draw from our extensive, life-long experiences with various objects and tasks in order to… (more)

Subjects/Keywords: Artificial intelligence; Cognitive robotics; Human-robot interaction; Transfer learning

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Fitzgerald, T. K. (2020). Human-guided task transfer for interactive robots. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/63630

Chicago Manual of Style (16th Edition):

Fitzgerald, Tesca Kate. “Human-guided task transfer for interactive robots.” 2020. Doctoral Dissertation, Georgia Tech. Accessed April 21, 2021. http://hdl.handle.net/1853/63630.

MLA Handbook (7th Edition):

Fitzgerald, Tesca Kate. “Human-guided task transfer for interactive robots.” 2020. Web. 21 Apr 2021.

Vancouver:

Fitzgerald TK. Human-guided task transfer for interactive robots. [Internet] [Doctoral dissertation]. Georgia Tech; 2020. [cited 2021 Apr 21]. Available from: http://hdl.handle.net/1853/63630.

Council of Science Editors:

Fitzgerald TK. Human-guided task transfer for interactive robots. [Doctoral Dissertation]. Georgia Tech; 2020. Available from: http://hdl.handle.net/1853/63630


Georgia Tech

14. Young, Carol C. Quantitative Analysis of Adaptiveness and Consistency of a Class of Online Learning Algorithms.

Degree: PhD, Electrical and Computer Engineering, 2019, Georgia Tech

 This thesis models online ensemble learning algorithms to obtain theoretical analyses of various performance metrics. Online ensemble learning algorithms often serve to learn unknown, possibly… (more)

Subjects/Keywords: Machine Learning; Markov chain; Learning algorithms

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Young, C. C. (2019). Quantitative Analysis of Adaptiveness and Consistency of a Class of Online Learning Algorithms. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/64012

Chicago Manual of Style (16th Edition):

Young, Carol C. “Quantitative Analysis of Adaptiveness and Consistency of a Class of Online Learning Algorithms.” 2019. Doctoral Dissertation, Georgia Tech. Accessed April 21, 2021. http://hdl.handle.net/1853/64012.

MLA Handbook (7th Edition):

Young, Carol C. “Quantitative Analysis of Adaptiveness and Consistency of a Class of Online Learning Algorithms.” 2019. Web. 21 Apr 2021.

Vancouver:

Young CC. Quantitative Analysis of Adaptiveness and Consistency of a Class of Online Learning Algorithms. [Internet] [Doctoral dissertation]. Georgia Tech; 2019. [cited 2021 Apr 21]. Available from: http://hdl.handle.net/1853/64012.

Council of Science Editors:

Young CC. Quantitative Analysis of Adaptiveness and Consistency of a Class of Online Learning Algorithms. [Doctoral Dissertation]. Georgia Tech; 2019. Available from: http://hdl.handle.net/1853/64012


Georgia Tech

15. 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

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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 21, 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. 21 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 21]. 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


Georgia Tech

16. Nair, Lakshmi Velayudhan. Robogyver: Autonomous Tool Macgyvering for Inventive Problem Solving.

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

 Robots that are situated in the real world are often faced with unforeseen situations that require them to adapt and improvise to be more useful.… (more)

Subjects/Keywords: Macgyvering; Creative problem solving; Multi-modal sensing; Adaptive robots

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Nair, L. V. (2020). Robogyver: Autonomous Tool Macgyvering for Inventive Problem Solving. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/64108

Chicago Manual of Style (16th Edition):

Nair, Lakshmi Velayudhan. “Robogyver: Autonomous Tool Macgyvering for Inventive Problem Solving.” 2020. Doctoral Dissertation, Georgia Tech. Accessed April 21, 2021. http://hdl.handle.net/1853/64108.

MLA Handbook (7th Edition):

Nair, Lakshmi Velayudhan. “Robogyver: Autonomous Tool Macgyvering for Inventive Problem Solving.” 2020. Web. 21 Apr 2021.

Vancouver:

Nair LV. Robogyver: Autonomous Tool Macgyvering for Inventive Problem Solving. [Internet] [Doctoral dissertation]. Georgia Tech; 2020. [cited 2021 Apr 21]. Available from: http://hdl.handle.net/1853/64108.

Council of Science Editors:

Nair LV. Robogyver: Autonomous Tool Macgyvering for Inventive Problem Solving. [Doctoral Dissertation]. Georgia Tech; 2020. Available from: http://hdl.handle.net/1853/64108

17. Setter, Tina M. Psychologically consistent coordinated control of multi-agent teams.

Degree: PhD, Electrical and Computer Engineering, 2017, Georgia Tech

 The objective of this research is to describe both human-robot interactions and inter-robot interactions and analyze the behavior of the resulting multi-agent systems, while drawing… (more)

Subjects/Keywords: Multi-agent systems; Human-robot interaction; Coordinated control

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Setter, T. M. (2017). Psychologically consistent coordinated control of multi-agent teams. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/58266

Chicago Manual of Style (16th Edition):

Setter, Tina M. “Psychologically consistent coordinated control of multi-agent teams.” 2017. Doctoral Dissertation, Georgia Tech. Accessed April 21, 2021. http://hdl.handle.net/1853/58266.

MLA Handbook (7th Edition):

Setter, Tina M. “Psychologically consistent coordinated control of multi-agent teams.” 2017. Web. 21 Apr 2021.

Vancouver:

Setter TM. Psychologically consistent coordinated control of multi-agent teams. [Internet] [Doctoral dissertation]. Georgia Tech; 2017. [cited 2021 Apr 21]. Available from: http://hdl.handle.net/1853/58266.

Council of Science Editors:

Setter TM. Psychologically consistent coordinated control of multi-agent teams. [Doctoral Dissertation]. Georgia Tech; 2017. Available from: http://hdl.handle.net/1853/58266

18. Mukadam, Mustafa. Structured learning and inference for robot motion generation.

Degree: PhD, Electrical and Computer Engineering, 2019, Georgia Tech

 The ability to generate motions that accomplish desired tasks is fundamental to any robotic system. Robots must be able to generate such motions in a… (more)

Subjects/Keywords: Motion planning; Machine learning

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Mukadam, M. (2019). Structured learning and inference for robot motion generation. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/61714

Chicago Manual of Style (16th Edition):

Mukadam, Mustafa. “Structured learning and inference for robot motion generation.” 2019. Doctoral Dissertation, Georgia Tech. Accessed April 21, 2021. http://hdl.handle.net/1853/61714.

MLA Handbook (7th Edition):

Mukadam, Mustafa. “Structured learning and inference for robot motion generation.” 2019. Web. 21 Apr 2021.

Vancouver:

Mukadam M. Structured learning and inference for robot motion generation. [Internet] [Doctoral dissertation]. Georgia Tech; 2019. [cited 2021 Apr 21]. Available from: http://hdl.handle.net/1853/61714.

Council of Science Editors:

Mukadam M. Structured learning and inference for robot motion generation. [Doctoral Dissertation]. Georgia Tech; 2019. Available from: http://hdl.handle.net/1853/61714

19. Shim, Jaeeun. The benefits of other-oriented robot deception in human-robot interaction.

Degree: PhD, Electrical and Computer Engineering, 2017, Georgia Tech

 Deception is an essential social behavior for humans, and we can observe human deceptive behaviors in a variety of contexts including sports, culture, education, war,… (more)

Subjects/Keywords: Human-robot interaction; Robot deception; Robot ethics

…and how it should deceive others. Recent work at Georgia Tech explored the role of deception… 

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Shim, J. (2017). The benefits of other-oriented robot deception in human-robot interaction. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/58253

Chicago Manual of Style (16th Edition):

Shim, Jaeeun. “The benefits of other-oriented robot deception in human-robot interaction.” 2017. Doctoral Dissertation, Georgia Tech. Accessed April 21, 2021. http://hdl.handle.net/1853/58253.

MLA Handbook (7th Edition):

Shim, Jaeeun. “The benefits of other-oriented robot deception in human-robot interaction.” 2017. Web. 21 Apr 2021.

Vancouver:

Shim J. The benefits of other-oriented robot deception in human-robot interaction. [Internet] [Doctoral dissertation]. Georgia Tech; 2017. [cited 2021 Apr 21]. Available from: http://hdl.handle.net/1853/58253.

Council of Science Editors:

Shim J. The benefits of other-oriented robot deception in human-robot interaction. [Doctoral Dissertation]. Georgia Tech; 2017. Available from: http://hdl.handle.net/1853/58253

20. Chu, Vivian. Teaching robots about human environments: Leveraging human interaction to efficiently learn and use multisensory object affordances.

Degree: PhD, Interactive Computing, 2018, Georgia Tech

 The real world is complex, unstructured, and contains high levels of uncertainty. Although past work shows that robots can successfully operate in situations where a… (more)

Subjects/Keywords: Robotics; Robot learning; Affordance learning; Human robot interaction; Multisensory data; Robot object manipulation; Human-guided robot exploration; Machine learning; Artificial intelligence; Haptics; Adaptable controllers; Multisensory robot control; Human-guided affordance learning; Interactive multisensory perception; Multimodal data; Sensor fusion

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Chu, V. (2018). Teaching robots about human environments: Leveraging human interaction to efficiently learn and use multisensory object affordances. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/59839

Chicago Manual of Style (16th Edition):

Chu, Vivian. “Teaching robots about human environments: Leveraging human interaction to efficiently learn and use multisensory object affordances.” 2018. Doctoral Dissertation, Georgia Tech. Accessed April 21, 2021. http://hdl.handle.net/1853/59839.

MLA Handbook (7th Edition):

Chu, Vivian. “Teaching robots about human environments: Leveraging human interaction to efficiently learn and use multisensory object affordances.” 2018. Web. 21 Apr 2021.

Vancouver:

Chu V. Teaching robots about human environments: Leveraging human interaction to efficiently learn and use multisensory object affordances. [Internet] [Doctoral dissertation]. Georgia Tech; 2018. [cited 2021 Apr 21]. Available from: http://hdl.handle.net/1853/59839.

Council of Science Editors:

Chu V. Teaching robots about human environments: Leveraging human interaction to efficiently learn and use multisensory object affordances. [Doctoral Dissertation]. Georgia Tech; 2018. Available from: http://hdl.handle.net/1853/59839

.