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You searched for +publisher:"University of Texas – Austin" +contributor:("Stone, Peter"). Showing records 1 – 22 of 22 total matches.

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University of Texas – Austin

1. -6950-6251. Light-based nonverbal signaling with passive demonstrations for mobile service robots.

Degree: MSin Computer Sciences, Computer Science, 2018, University of Texas – Austin

 With emerging applications in robotics that have the potential to bring them into our daily lives, it is expected for them to not only operate… (more)

Subjects/Keywords: Robotics; Human-robot interaction; Nonverbal signaling; Light-based signals; Mobile service robotics; Passive demonstrations

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APA (6th Edition):

-6950-6251. (2018). Light-based nonverbal signaling with passive demonstrations for mobile service robots. (Masters Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/68210

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Chicago Manual of Style (16th Edition):

-6950-6251. “Light-based nonverbal signaling with passive demonstrations for mobile service robots.” 2018. Masters Thesis, University of Texas – Austin. Accessed August 13, 2020. http://hdl.handle.net/2152/68210.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

MLA Handbook (7th Edition):

-6950-6251. “Light-based nonverbal signaling with passive demonstrations for mobile service robots.” 2018. Web. 13 Aug 2020.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Vancouver:

-6950-6251. Light-based nonverbal signaling with passive demonstrations for mobile service robots. [Internet] [Masters thesis]. University of Texas – Austin; 2018. [cited 2020 Aug 13]. Available from: http://hdl.handle.net/2152/68210.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Council of Science Editors:

-6950-6251. Light-based nonverbal signaling with passive demonstrations for mobile service robots. [Masters Thesis]. University of Texas – Austin; 2018. Available from: http://hdl.handle.net/2152/68210

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete


University of Texas – Austin

2. Barrett, Samuel Rubin. Making friends on the fly : advances in ad hoc teamwork.

Degree: PhD, Computer Science, 2014, University of Texas – Austin

 Given the continuing improvements in design and manufacturing processes in addition to improvements in artificial intelligence, robots are being deployed in an increasing variety of… (more)

Subjects/Keywords: Ad hoc teamwork; Multiagent systems; Machine learning

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APA (6th Edition):

Barrett, S. R. (2014). Making friends on the fly : advances in ad hoc teamwork. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/28328

Chicago Manual of Style (16th Edition):

Barrett, Samuel Rubin. “Making friends on the fly : advances in ad hoc teamwork.” 2014. Doctoral Dissertation, University of Texas – Austin. Accessed August 13, 2020. http://hdl.handle.net/2152/28328.

MLA Handbook (7th Edition):

Barrett, Samuel Rubin. “Making friends on the fly : advances in ad hoc teamwork.” 2014. Web. 13 Aug 2020.

Vancouver:

Barrett SR. Making friends on the fly : advances in ad hoc teamwork. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2014. [cited 2020 Aug 13]. Available from: http://hdl.handle.net/2152/28328.

Council of Science Editors:

Barrett SR. Making friends on the fly : advances in ad hoc teamwork. [Doctoral Dissertation]. University of Texas – Austin; 2014. Available from: http://hdl.handle.net/2152/28328


University of Texas – Austin

3. Khante, Priyanka. Learning attributes of real-world objects by clustering multimodal sensory data.

Degree: MSin Computer Sciences, Computer Science, 2017, University of Texas – Austin

 The goal of this work is to propose a framework for learning attributes of real-world objects via a clustering-based approach that aims to reduce the… (more)

Subjects/Keywords: Robot; Multi-modal sensory data; Clustering; Attribute labelling; Human effort

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APA (6th Edition):

Khante, P. (2017). Learning attributes of real-world objects by clustering multimodal sensory data. (Masters Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/62887

Chicago Manual of Style (16th Edition):

Khante, Priyanka. “Learning attributes of real-world objects by clustering multimodal sensory data.” 2017. Masters Thesis, University of Texas – Austin. Accessed August 13, 2020. http://hdl.handle.net/2152/62887.

MLA Handbook (7th Edition):

Khante, Priyanka. “Learning attributes of real-world objects by clustering multimodal sensory data.” 2017. Web. 13 Aug 2020.

Vancouver:

Khante P. Learning attributes of real-world objects by clustering multimodal sensory data. [Internet] [Masters thesis]. University of Texas – Austin; 2017. [cited 2020 Aug 13]. Available from: http://hdl.handle.net/2152/62887.

Council of Science Editors:

Khante P. Learning attributes of real-world objects by clustering multimodal sensory data. [Masters Thesis]. University of Texas – Austin; 2017. Available from: http://hdl.handle.net/2152/62887


University of Texas – Austin

4. -7041-9136. Evolutionary bilevel optimization for complex control problems and blackbox function optimization.

Degree: MSin Computer Sciences, Computer Science, 2015, University of Texas – Austin

 Most optimization algorithms must undergo time consuming parameter tuning in order to solve complex, real-world control tasks. Parameter tuning is inherently a bilevel optimization problem:… (more)

Subjects/Keywords: Genetic algorithms; Metaheuristics; Neural networks; Fitness approximation; Parameter tuning

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APA (6th Edition):

-7041-9136. (2015). Evolutionary bilevel optimization for complex control problems and blackbox function optimization. (Masters Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/31848

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Chicago Manual of Style (16th Edition):

-7041-9136. “Evolutionary bilevel optimization for complex control problems and blackbox function optimization.” 2015. Masters Thesis, University of Texas – Austin. Accessed August 13, 2020. http://hdl.handle.net/2152/31848.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

MLA Handbook (7th Edition):

-7041-9136. “Evolutionary bilevel optimization for complex control problems and blackbox function optimization.” 2015. Web. 13 Aug 2020.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Vancouver:

-7041-9136. Evolutionary bilevel optimization for complex control problems and blackbox function optimization. [Internet] [Masters thesis]. University of Texas – Austin; 2015. [cited 2020 Aug 13]. Available from: http://hdl.handle.net/2152/31848.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Council of Science Editors:

-7041-9136. Evolutionary bilevel optimization for complex control problems and blackbox function optimization. [Masters Thesis]. University of Texas – Austin; 2015. Available from: http://hdl.handle.net/2152/31848

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

5. -3115-1189. Fly with me : algorithms and methods for influencing a flock.

Degree: PhD, Computer Science, 2017, University of Texas – Austin

 As robots become more affordable, they will begin to exist in the world in greater quantities. Some of these robots will likely be designed to… (more)

Subjects/Keywords: Flocking; Ad hoc teamwork; Influencing a flock; Ad hoc agent; Multiagent teamwork; Swarms

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APA (6th Edition):

-3115-1189. (2017). Fly with me : algorithms and methods for influencing a flock. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/61905

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Chicago Manual of Style (16th Edition):

-3115-1189. “Fly with me : algorithms and methods for influencing a flock.” 2017. Doctoral Dissertation, University of Texas – Austin. Accessed August 13, 2020. http://hdl.handle.net/2152/61905.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

MLA Handbook (7th Edition):

-3115-1189. “Fly with me : algorithms and methods for influencing a flock.” 2017. Web. 13 Aug 2020.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Vancouver:

-3115-1189. Fly with me : algorithms and methods for influencing a flock. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2017. [cited 2020 Aug 13]. Available from: http://hdl.handle.net/2152/61905.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Council of Science Editors:

-3115-1189. Fly with me : algorithms and methods for influencing a flock. [Doctoral Dissertation]. University of Texas – Austin; 2017. Available from: http://hdl.handle.net/2152/61905

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

6. -7041-9136. Evolutionary neural architecture search for deep learning.

Degree: PhD, Computer Science, 2019, University of Texas – Austin

 Deep neural networks (DNNs) have produced state-of-the-art results in many benchmarks and problem domains. However, the success of DNNs depends on the proper configuration of… (more)

Subjects/Keywords: Neural architecture search; Deep learning; Neuroevolution; Evolutionary computation; Artificial intelligence

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APA (6th Edition):

-7041-9136. (2019). Evolutionary neural architecture search for deep learning. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://dx.doi.org/10.26153/tsw/1388

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Chicago Manual of Style (16th Edition):

-7041-9136. “Evolutionary neural architecture search for deep learning.” 2019. Doctoral Dissertation, University of Texas – Austin. Accessed August 13, 2020. http://dx.doi.org/10.26153/tsw/1388.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

MLA Handbook (7th Edition):

-7041-9136. “Evolutionary neural architecture search for deep learning.” 2019. Web. 13 Aug 2020.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Vancouver:

-7041-9136. Evolutionary neural architecture search for deep learning. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2019. [cited 2020 Aug 13]. Available from: http://dx.doi.org/10.26153/tsw/1388.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Council of Science Editors:

-7041-9136. Evolutionary neural architecture search for deep learning. [Doctoral Dissertation]. University of Texas – Austin; 2019. Available from: http://dx.doi.org/10.26153/tsw/1388

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete


University of Texas – Austin

7. -8485-1448. Intelligent collision management in dynamic environments for human-centered robots.

Degree: PhD, Mechanical Engineering, 2017, University of Texas – Austin

 Thanks to rapid breakthroughs on robotics, their historical deployment in industrial setups, their current extensions to warehouses, and the highly anticipated deployment of autonomous vehicles… (more)

Subjects/Keywords: Robotics; Safety

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APA (6th Edition):

-8485-1448. (2017). Intelligent collision management in dynamic environments for human-centered robots. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/63643

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Chicago Manual of Style (16th Edition):

-8485-1448. “Intelligent collision management in dynamic environments for human-centered robots.” 2017. Doctoral Dissertation, University of Texas – Austin. Accessed August 13, 2020. http://hdl.handle.net/2152/63643.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

MLA Handbook (7th Edition):

-8485-1448. “Intelligent collision management in dynamic environments for human-centered robots.” 2017. Web. 13 Aug 2020.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Vancouver:

-8485-1448. Intelligent collision management in dynamic environments for human-centered robots. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2017. [cited 2020 Aug 13]. Available from: http://hdl.handle.net/2152/63643.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Council of Science Editors:

-8485-1448. Intelligent collision management in dynamic environments for human-centered robots. [Doctoral Dissertation]. University of Texas – Austin; 2017. Available from: http://hdl.handle.net/2152/63643

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete


University of Texas – Austin

8. -8073-3276. Parameterized modular inverse reinforcement learning.

Degree: MSin Computer Sciences, Computer Science, 2015, University of Texas – Austin

 Reinforcement learning and inverse reinforcement learning can be used to model and understand human behaviors. However, due to the curse of dimensionality, their use as… (more)

Subjects/Keywords: Reinforcement learning; Artificial intelligence; Inverse reinforcement learning; Modular inverse reinforcement learning; Reinforcement learning algorithms; Human navigation behaviors

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APA (6th Edition):

-8073-3276. (2015). Parameterized modular inverse reinforcement learning. (Masters Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/46987

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Chicago Manual of Style (16th Edition):

-8073-3276. “Parameterized modular inverse reinforcement learning.” 2015. Masters Thesis, University of Texas – Austin. Accessed August 13, 2020. http://hdl.handle.net/2152/46987.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

MLA Handbook (7th Edition):

-8073-3276. “Parameterized modular inverse reinforcement learning.” 2015. Web. 13 Aug 2020.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Vancouver:

-8073-3276. Parameterized modular inverse reinforcement learning. [Internet] [Masters thesis]. University of Texas – Austin; 2015. [cited 2020 Aug 13]. Available from: http://hdl.handle.net/2152/46987.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Council of Science Editors:

-8073-3276. Parameterized modular inverse reinforcement learning. [Masters Thesis]. University of Texas – Austin; 2015. Available from: http://hdl.handle.net/2152/46987

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete


University of Texas – Austin

9. -7411-0398. Data efficient reinforcement learning with off-policy and simulated data.

Degree: PhD, Computer Science, 2019, University of Texas – Austin

 Learning from interaction with the environment  – trying untested actions, observing successes and failures, and tying effects back to causes  – is one of the… (more)

Subjects/Keywords: Artificial intelligence; Reinforcement learning; Robotics; Off-policy; Sim-to-real

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APA (6th Edition):

-7411-0398. (2019). Data efficient reinforcement learning with off-policy and simulated data. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://dx.doi.org/10.26153/tsw/7716

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Chicago Manual of Style (16th Edition):

-7411-0398. “Data efficient reinforcement learning with off-policy and simulated data.” 2019. Doctoral Dissertation, University of Texas – Austin. Accessed August 13, 2020. http://dx.doi.org/10.26153/tsw/7716.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

MLA Handbook (7th Edition):

-7411-0398. “Data efficient reinforcement learning with off-policy and simulated data.” 2019. Web. 13 Aug 2020.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Vancouver:

-7411-0398. Data efficient reinforcement learning with off-policy and simulated data. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2019. [cited 2020 Aug 13]. Available from: http://dx.doi.org/10.26153/tsw/7716.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Council of Science Editors:

-7411-0398. Data efficient reinforcement learning with off-policy and simulated data. [Doctoral Dissertation]. University of Texas – Austin; 2019. Available from: http://dx.doi.org/10.26153/tsw/7716

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

10. Liebman, Elad. Sequential decision making in artificial musical intelligence.

Degree: PhD, Computer Science, 2019, University of Texas – Austin

 Over the past 60 years, artificial intelligence has grown from a largely academic field of research to a ubiquitous array of tools and approaches used… (more)

Subjects/Keywords: Music informatics; Reinforcement learning; Artificial intelligence; Sequential decision-making

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APA (6th Edition):

Liebman, E. (2019). Sequential decision making in artificial musical intelligence. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://dx.doi.org/10.26153/tsw/2858

Chicago Manual of Style (16th Edition):

Liebman, Elad. “Sequential decision making in artificial musical intelligence.” 2019. Doctoral Dissertation, University of Texas – Austin. Accessed August 13, 2020. http://dx.doi.org/10.26153/tsw/2858.

MLA Handbook (7th Edition):

Liebman, Elad. “Sequential decision making in artificial musical intelligence.” 2019. Web. 13 Aug 2020.

Vancouver:

Liebman E. Sequential decision making in artificial musical intelligence. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2019. [cited 2020 Aug 13]. Available from: http://dx.doi.org/10.26153/tsw/2858.

Council of Science Editors:

Liebman E. Sequential decision making in artificial musical intelligence. [Doctoral Dissertation]. University of Texas – Austin; 2019. Available from: http://dx.doi.org/10.26153/tsw/2858


University of Texas – Austin

11. Chen, Chao-Yeh. Learning human activities and poses with interconnected data sources.

Degree: PhD, Computer science, 2016, University of Texas – Austin

 Understanding human actions and poses in images or videos is a challenging problem in computer vision. There are different topics related to this problem such… (more)

Subjects/Keywords: Activity recognition; Activity detection

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APA (6th Edition):

Chen, C. (2016). Learning human activities and poses with interconnected data sources. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/40260

Chicago Manual of Style (16th Edition):

Chen, Chao-Yeh. “Learning human activities and poses with interconnected data sources.” 2016. Doctoral Dissertation, University of Texas – Austin. Accessed August 13, 2020. http://hdl.handle.net/2152/40260.

MLA Handbook (7th Edition):

Chen, Chao-Yeh. “Learning human activities and poses with interconnected data sources.” 2016. Web. 13 Aug 2020.

Vancouver:

Chen C. Learning human activities and poses with interconnected data sources. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2016. [cited 2020 Aug 13]. Available from: http://hdl.handle.net/2152/40260.

Council of Science Editors:

Chen C. Learning human activities and poses with interconnected data sources. [Doctoral Dissertation]. University of Texas – Austin; 2016. Available from: http://hdl.handle.net/2152/40260


University of Texas – Austin

12. -7062-2970. Advances in statistical script learning.

Degree: PhD, Computer Science, 2017, University of Texas – Austin

 When humans encode information into natural language, they do so with the clear assumption that the reader will be able to seamlessly make inferences based… (more)

Subjects/Keywords: Natural language processing; Machine learning

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APA (6th Edition):

-7062-2970. (2017). Advances in statistical script learning. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/63480

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Chicago Manual of Style (16th Edition):

-7062-2970. “Advances in statistical script learning.” 2017. Doctoral Dissertation, University of Texas – Austin. Accessed August 13, 2020. http://hdl.handle.net/2152/63480.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

MLA Handbook (7th Edition):

-7062-2970. “Advances in statistical script learning.” 2017. Web. 13 Aug 2020.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Vancouver:

-7062-2970. Advances in statistical script learning. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2017. [cited 2020 Aug 13]. Available from: http://hdl.handle.net/2152/63480.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Council of Science Editors:

-7062-2970. Advances in statistical script learning. [Doctoral Dissertation]. University of Texas – Austin; 2017. Available from: http://hdl.handle.net/2152/63480

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete


University of Texas – Austin

13. Stober, Jeremy Michael. Sensorimotor embedding : a developmental approach to learning geometry.

Degree: PhD, Computer Science, 2015, University of Texas – Austin

 A human infant facing the blooming, buzzing confusion of the senses grows up to be an adult with common-sense knowledge of geometry; this knowledge then… (more)

Subjects/Keywords: Sensorimotor; Ai; Robotics; Development

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APA (6th Edition):

Stober, J. M. (2015). Sensorimotor embedding : a developmental approach to learning geometry. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/30532

Chicago Manual of Style (16th Edition):

Stober, Jeremy Michael. “Sensorimotor embedding : a developmental approach to learning geometry.” 2015. Doctoral Dissertation, University of Texas – Austin. Accessed August 13, 2020. http://hdl.handle.net/2152/30532.

MLA Handbook (7th Edition):

Stober, Jeremy Michael. “Sensorimotor embedding : a developmental approach to learning geometry.” 2015. Web. 13 Aug 2020.

Vancouver:

Stober JM. Sensorimotor embedding : a developmental approach to learning geometry. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2015. [cited 2020 Aug 13]. Available from: http://hdl.handle.net/2152/30532.

Council of Science Editors:

Stober JM. Sensorimotor embedding : a developmental approach to learning geometry. [Doctoral Dissertation]. University of Texas – Austin; 2015. Available from: http://hdl.handle.net/2152/30532

14. -9534-5383. Sensor-based robust whole-body control of highly dynamic legged humanoid robots.

Degree: PhD, Mechanical Engineering, 2018, University of Texas – Austin

 Industrial robots significantly improve the productivity of manufacturing operations performing various tasks rapidly, accurately, and repeatedly. It would be hard to imagine factories without robotic… (more)

Subjects/Keywords: Whole-body control; Biped locomotion; Dynamic motion control; Torque control

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APA (6th Edition):

-9534-5383. (2018). Sensor-based robust whole-body control of highly dynamic legged humanoid robots. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/68181

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Chicago Manual of Style (16th Edition):

-9534-5383. “Sensor-based robust whole-body control of highly dynamic legged humanoid robots.” 2018. Doctoral Dissertation, University of Texas – Austin. Accessed August 13, 2020. http://hdl.handle.net/2152/68181.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

MLA Handbook (7th Edition):

-9534-5383. “Sensor-based robust whole-body control of highly dynamic legged humanoid robots.” 2018. Web. 13 Aug 2020.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Vancouver:

-9534-5383. Sensor-based robust whole-body control of highly dynamic legged humanoid robots. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2018. [cited 2020 Aug 13]. Available from: http://hdl.handle.net/2152/68181.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Council of Science Editors:

-9534-5383. Sensor-based robust whole-body control of highly dynamic legged humanoid robots. [Doctoral Dissertation]. University of Texas – Austin; 2018. Available from: http://hdl.handle.net/2152/68181

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete


University of Texas – Austin

15. -6763-2625. Multilayered skill learning and movement coordination for autonomous robotic agents.

Degree: PhD, Computer Science, 2017, University of Texas – Austin

 With advances in technology expanding the capabilities of robots, while at the same time making robots cheaper to manufacture, robots are rapidly becoming more prevalent… (more)

Subjects/Keywords: Overlapping layered learning; Role assignment; Reinforcement learning; Robotics; Robot soccer

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APA (6th Edition):

-6763-2625. (2017). Multilayered skill learning and movement coordination for autonomous robotic agents. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/62889

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Chicago Manual of Style (16th Edition):

-6763-2625. “Multilayered skill learning and movement coordination for autonomous robotic agents.” 2017. Doctoral Dissertation, University of Texas – Austin. Accessed August 13, 2020. http://hdl.handle.net/2152/62889.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

MLA Handbook (7th Edition):

-6763-2625. “Multilayered skill learning and movement coordination for autonomous robotic agents.” 2017. Web. 13 Aug 2020.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Vancouver:

-6763-2625. Multilayered skill learning and movement coordination for autonomous robotic agents. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2017. [cited 2020 Aug 13]. Available from: http://hdl.handle.net/2152/62889.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Council of Science Editors:

-6763-2625. Multilayered skill learning and movement coordination for autonomous robotic agents. [Doctoral Dissertation]. University of Texas – Austin; 2017. Available from: http://hdl.handle.net/2152/62889

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16. -3729-8456. Natural-language video description with deep recurrent neural networks.

Degree: PhD, Computer Science, 2017, University of Texas – Austin

 For most people, watching a brief video and describing what happened (in words) is an easy task. For machines, extracting meaning from video pixels and… (more)

Subjects/Keywords: Video; Captioning; Description; LSTM; RNN; Recurrent; Neural networks; Image captioning; Video captioning; Language and vision

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APA (6th Edition):

-3729-8456. (2017). Natural-language video description with deep recurrent neural networks. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/62987

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Chicago Manual of Style (16th Edition):

-3729-8456. “Natural-language video description with deep recurrent neural networks.” 2017. Doctoral Dissertation, University of Texas – Austin. Accessed August 13, 2020. http://hdl.handle.net/2152/62987.

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Author name may be incomplete

MLA Handbook (7th Edition):

-3729-8456. “Natural-language video description with deep recurrent neural networks.” 2017. Web. 13 Aug 2020.

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Author name may be incomplete

Vancouver:

-3729-8456. Natural-language video description with deep recurrent neural networks. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2017. [cited 2020 Aug 13]. Available from: http://hdl.handle.net/2152/62987.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Council of Science Editors:

-3729-8456. Natural-language video description with deep recurrent neural networks. [Doctoral Dissertation]. University of Texas – Austin; 2017. Available from: http://hdl.handle.net/2152/62987

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Author name may be incomplete


University of Texas – Austin

17. Huang, Pei-Chi. Real-time robotic tasks for cyber-physical avatars.

Degree: PhD, Computer Science, 2017, University of Texas – Austin

 Although modern robots can perform complex tasks using sophisticated algorithms that are specialized to a particular task and environment, creating robots capable of completing tasks… (more)

Subjects/Keywords: Cyber-physical systems; Robotics; Evolutionary computation; Machine learning; Real-time systems; Deep learning; Computer vision; Control

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APA (6th Edition):

Huang, P. (2017). Real-time robotic tasks for cyber-physical avatars. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/62985

Chicago Manual of Style (16th Edition):

Huang, Pei-Chi. “Real-time robotic tasks for cyber-physical avatars.” 2017. Doctoral Dissertation, University of Texas – Austin. Accessed August 13, 2020. http://hdl.handle.net/2152/62985.

MLA Handbook (7th Edition):

Huang, Pei-Chi. “Real-time robotic tasks for cyber-physical avatars.” 2017. Web. 13 Aug 2020.

Vancouver:

Huang P. Real-time robotic tasks for cyber-physical avatars. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2017. [cited 2020 Aug 13]. Available from: http://hdl.handle.net/2152/62985.

Council of Science Editors:

Huang P. Real-time robotic tasks for cyber-physical avatars. [Doctoral Dissertation]. University of Texas – Austin; 2017. Available from: http://hdl.handle.net/2152/62985

18. Levin, Michael William. Modeling and optimizing network infrastructure for autonomous vehicles.

Degree: PhD, Civil Engineering, 2017, University of Texas – Austin

 Autonomous vehicle (AV) technology has matured sufficiently to be in testing on public roads. However, traffic models of AVs are still in development. Most previous… (more)

Subjects/Keywords: Autonomous vehicles; Dynamic traffic assignment; Traffic flow; Dynamic lane reversal; Reservation-based intersection control

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APA (6th Edition):

Levin, M. W. (2017). Modeling and optimizing network infrastructure for autonomous vehicles. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/47311

Chicago Manual of Style (16th Edition):

Levin, Michael William. “Modeling and optimizing network infrastructure for autonomous vehicles.” 2017. Doctoral Dissertation, University of Texas – Austin. Accessed August 13, 2020. http://hdl.handle.net/2152/47311.

MLA Handbook (7th Edition):

Levin, Michael William. “Modeling and optimizing network infrastructure for autonomous vehicles.” 2017. Web. 13 Aug 2020.

Vancouver:

Levin MW. Modeling and optimizing network infrastructure for autonomous vehicles. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2017. [cited 2020 Aug 13]. Available from: http://hdl.handle.net/2152/47311.

Council of Science Editors:

Levin MW. Modeling and optimizing network infrastructure for autonomous vehicles. [Doctoral Dissertation]. University of Texas – Austin; 2017. Available from: http://hdl.handle.net/2152/47311

19. Ravula, Manish Chandra Reddy. Ad-hoc teamwork with behavior-switching agents.

Degree: MSin Engineering, Electrical and Computer Engineering, 2019, University of Texas – Austin

 As autonomous AI agents proliferate in the real world, they will increasingly need to cooperate with each other to achieve complex goals without always being… (more)

Subjects/Keywords: Machine learning

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APA (6th Edition):

Ravula, M. C. R. (2019). Ad-hoc teamwork with behavior-switching agents. (Masters Thesis). University of Texas – Austin. Retrieved from http://dx.doi.org/10.26153/tsw/5574

Chicago Manual of Style (16th Edition):

Ravula, Manish Chandra Reddy. “Ad-hoc teamwork with behavior-switching agents.” 2019. Masters Thesis, University of Texas – Austin. Accessed August 13, 2020. http://dx.doi.org/10.26153/tsw/5574.

MLA Handbook (7th Edition):

Ravula, Manish Chandra Reddy. “Ad-hoc teamwork with behavior-switching agents.” 2019. Web. 13 Aug 2020.

Vancouver:

Ravula MCR. Ad-hoc teamwork with behavior-switching agents. [Internet] [Masters thesis]. University of Texas – Austin; 2019. [cited 2020 Aug 13]. Available from: http://dx.doi.org/10.26153/tsw/5574.

Council of Science Editors:

Ravula MCR. Ad-hoc teamwork with behavior-switching agents. [Masters Thesis]. University of Texas – Austin; 2019. Available from: http://dx.doi.org/10.26153/tsw/5574

20. -5294-4228. Scalable smoothing algorithms for massive graph-structured data.

Degree: PhD, Computer Science, 2017, University of Texas – Austin

 Probabilistically modeling noisy data is a crucial step in virtually all scientific experiments and engineering pipelines. Recent years have seen the rise of several high-throughput… (more)

Subjects/Keywords: Smoothing; Algorithms; False discovery rate; Spatial smoothing; Total variation; Trend filtering

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APA (6th Edition):

-5294-4228. (2017). Scalable smoothing algorithms for massive graph-structured data. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/61823

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Chicago Manual of Style (16th Edition):

-5294-4228. “Scalable smoothing algorithms for massive graph-structured data.” 2017. Doctoral Dissertation, University of Texas – Austin. Accessed August 13, 2020. http://hdl.handle.net/2152/61823.

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Author name may be incomplete

MLA Handbook (7th Edition):

-5294-4228. “Scalable smoothing algorithms for massive graph-structured data.” 2017. Web. 13 Aug 2020.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Vancouver:

-5294-4228. Scalable smoothing algorithms for massive graph-structured data. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2017. [cited 2020 Aug 13]. Available from: http://hdl.handle.net/2152/61823.

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Author name may be incomplete

Council of Science Editors:

-5294-4228. Scalable smoothing algorithms for massive graph-structured data. [Doctoral Dissertation]. University of Texas – Austin; 2017. Available from: http://hdl.handle.net/2152/61823

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Author name may be incomplete

21. -9199-0633. Continually improving grounded natural language understanding through human-robot dialog.

Degree: PhD, Computer Science, 2018, University of Texas – Austin

 As robots become ubiquitous in homes and workplaces such as hospitals and factories, they must be able to communicate with humans. Several kinds of knowledge… (more)

Subjects/Keywords: Natural language processing; Human-robot dialog

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APA (6th Edition):

-9199-0633. (2018). Continually improving grounded natural language understanding through human-robot dialog. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/68120

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Chicago Manual of Style (16th Edition):

-9199-0633. “Continually improving grounded natural language understanding through human-robot dialog.” 2018. Doctoral Dissertation, University of Texas – Austin. Accessed August 13, 2020. http://hdl.handle.net/2152/68120.

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Author name may be incomplete

MLA Handbook (7th Edition):

-9199-0633. “Continually improving grounded natural language understanding through human-robot dialog.” 2018. Web. 13 Aug 2020.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Vancouver:

-9199-0633. Continually improving grounded natural language understanding through human-robot dialog. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2018. [cited 2020 Aug 13]. Available from: http://hdl.handle.net/2152/68120.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Council of Science Editors:

-9199-0633. Continually improving grounded natural language understanding through human-robot dialog. [Doctoral Dissertation]. University of Texas – Austin; 2018. Available from: http://hdl.handle.net/2152/68120

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22. Khandelwal, Piyush. On-demand coordination of multiple service robots.

Degree: PhD, Computer Science, 2017, University of Texas – Austin

 Research in recent years has made it increasingly plausible to deploy a large number of service robots in home and office environments. Given that multiple… (more)

Subjects/Keywords: Multi-robot coordination; Monte Carlo tree search; Markov decision processes; Probabilistic planning; Multi-robot systems

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

APA (6th Edition):

Khandelwal, P. (2017). On-demand coordination of multiple service robots. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/61382

Chicago Manual of Style (16th Edition):

Khandelwal, Piyush. “On-demand coordination of multiple service robots.” 2017. Doctoral Dissertation, University of Texas – Austin. Accessed August 13, 2020. http://hdl.handle.net/2152/61382.

MLA Handbook (7th Edition):

Khandelwal, Piyush. “On-demand coordination of multiple service robots.” 2017. Web. 13 Aug 2020.

Vancouver:

Khandelwal P. On-demand coordination of multiple service robots. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2017. [cited 2020 Aug 13]. Available from: http://hdl.handle.net/2152/61382.

Council of Science Editors:

Khandelwal P. On-demand coordination of multiple service robots. [Doctoral Dissertation]. University of Texas – Austin; 2017. Available from: http://hdl.handle.net/2152/61382

.