You searched for +publisher:"University of Texas – Austin" +contributor:("Niekum, Scott").
Showing records 1 – 8 of
8 total matches.
No search limiters apply to these results.
University of Texas – Austin
1.
-4766-7868.
Self-learning control of automated drilling operations.
Degree: Mechanical Engineering, 2018, University of Texas – Austin
URL: http://hdl.handle.net/2152/65829 
► In recent years, drilling automation has sparked significant interest in both the upstream oil and gas industry and the drilling research community. Automation of various…
(more)
▼ In recent years, drilling automation has sparked significant interest in both the upstream oil and gas industry and the drilling research community. Automation of various drilling tasks can potentially allow for higher operational efficiency, increased consistency, and reduced risk of trouble events. However, wide adoption of drilling automation has been slow. This can be primarily attributed to the complex nature of drilling, and the high variability in well types and rig specifications that prevent the deployment of off-the-shelf automation solutions. Such complexities justify the need for an automation system that can self-learn by interacting with the drilling environment to reduce uncertainty.
The aim of this dissertation is to determine how a drilling automation system can learn from the environment and utilize this learning to control drilling tasks optimally. To provide an answer, the importance of learning, as well as its limitations in dealing with challenges such as insufficient training data, are explored.
A self-learning control system is presented that addresses the aforementioned research question in the context of optimization, control, and event detection. By adopting an action-driven learning approach, the control system can learn the parameters that describe system dynamics. An action-driven approach is shown to also enable the learning of the relationship between control actions and user-defined performance metrics. The resulting knowledge of this learning process enables the system to make and execute optimal decisions without relying on simplifying assumptions that are often made in the drilling literature. Detection of trouble drilling events is explored, and methods for reduction of false/missed alarms are presented to minimize false interruptions of the drilling control system. The subcomponents of the self-learning control system are validated using simulated and actual field data from drilling operations to ascertain the effectiveness of the proposed methods.
Advisors/Committee Members: Van Oort, Eric (advisor), Fernandez, Benito R. (advisor), Chen, Dongmei (committee member), Barr, Ronald E. (committee member), Niekum, Scott (committee member).
Subjects/Keywords: Automated drilling; Drilling optimization; Self-learning control
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
-4766-7868. (2018). Self-learning control of automated drilling operations. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/65829
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
-4766-7868. “Self-learning control of automated drilling operations.” 2018. Thesis, University of Texas – Austin. Accessed February 16, 2019.
http://hdl.handle.net/2152/65829.
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
-4766-7868. “Self-learning control of automated drilling operations.” 2018. Web. 16 Feb 2019.
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Vancouver:
-4766-7868. Self-learning control of automated drilling operations. [Internet] [Thesis]. University of Texas – Austin; 2018. [cited 2019 Feb 16].
Available from: http://hdl.handle.net/2152/65829.
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
-4766-7868. Self-learning control of automated drilling operations. [Thesis]. University of Texas – Austin; 2018. Available from: http://hdl.handle.net/2152/65829
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Not specified: Masters Thesis or Doctoral Dissertation
University of Texas – Austin
2.
-9199-0633.
Continually improving grounded natural language understanding through human-robot dialog.
Degree: Computer Sciences, 2018, University of Texas – Austin
URL: http://hdl.handle.net/2152/68120
► 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)
▼ 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 are required to understand and respond to a human's natural language commands and questions. If a person requests an assistant robot to take me to Alice's office, the robot must know that Alice is a person who owns some unique office, and that take me means it should navigate there. Similarly, if a person requests bring me the heavy, green mug, the robot must have accurate mental models of the physical concepts heavy, green, and mug. To avoid forcing humans to use key phrases or words robots already know, this thesis focuses on helping robots understanding new language constructs through interactions with humans and with the world around them. To understand a command in natural language, a robot must first convert that command to an internal representation that it can reason with. Semantic parsing is a method for performing this conversion, and the target representation is often semantic forms represented as predicate logic with lambda calculus. Traditional semantic parsing relies on hand-crafted resources from a human expert: an ontology of concepts, a lexicon connecting language to those concepts, and training examples of language with abstract meanings. One thrust of this thesis is to perform semantic parsing with sparse initial data. We use the conversations between a robot and human users to induce pairs of natural language utterances with the target semantic forms a robot discovers through its questions, reducing the annotation effort of creating training examples for parsing. We use this data to build more dialog-capable robots in new domains with much less expert human effort (Thomason et al., 2015; Padmakumar et al., 2017). Meanings of many language concepts are bound to the physical world. Understanding object properties and categories, such as heavy, green, and mug requires interacting with and perceiving the physical world. Embodied robots can use manipulation capabilities, such as pushing, picking up, and dropping objects to gather sensory data about them. This data can be used to understand non-visual concepts like heavy and empty (e.g. get the empty carton of milk from the fridge), and assist with concepts that have both visual and non-visual expression (e.g. tall things look big and also exert force sooner than short things when pressed down on). A second thrust of this thesis focuses on strategies for learning these concepts using multi-modal sensory information. We use human-in-the-loop learning to get labels between concept words and actual objects in the environment (Thomason et al., 2016, 2017). We also explore ways to tease out polysemy and synonymy in concept words (Thomason and Mooney, 2017) such as light, which can refer to a weight or a color, the latter sense being synonymous with pale. Additionally, pushing, picking up, and dropping objects to gather sensory information is prohibitively time-consuming, so we investigate strategies for…
Advisors/Committee Members: Mooney, Raymond J. (Raymond Joseph) (advisor), Stone, Peter (committee member), Niekum, Scott (committee member), Tellex, Stefanie (committee member).
Subjects/Keywords: Natural language processing; Human-robot dialog
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
-9199-0633. (2018). Continually improving grounded natural language understanding through human-robot dialog. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/68120
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
-9199-0633. “Continually improving grounded natural language understanding through human-robot dialog.” 2018. Thesis, University of Texas – Austin. Accessed February 16, 2019.
http://hdl.handle.net/2152/68120.
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
-9199-0633. “Continually improving grounded natural language understanding through human-robot dialog.” 2018. Web. 16 Feb 2019.
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] [Thesis]. University of Texas – Austin; 2018. [cited 2019 Feb 16].
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
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
-9199-0633. Continually improving grounded natural language understanding through human-robot dialog. [Thesis]. University of Texas – Austin; 2018. 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
Not specified: Masters Thesis or Doctoral Dissertation
3.
Chiang, Kai-Yang.
Statistical analysis for modeling dyadic interactions using machine learning methods.
Degree: Computer Sciences, 2017, University of Texas – Austin
URL: http://hdl.handle.net/2152/47368
► Modeling dyadic interactions between entities is one of the fundamental problems in machine learning with many real-world applications, including recommender systems, data clustering, social network…
(more)
▼ Modeling dyadic interactions between entities is one of the fundamental problems in machine learning with many real-world applications, including recommender systems, data clustering, social network analysis and ranking. In this dissertation, we introduce several improved models for modeling dyadic interactions in machine learning by taking advantage of sophisticated information from different sources, such as prior structure, domain knowledge and side information. We start with exploiting different types of auxiliary information for several motivating applications, including signed link prediction, signed graph clustering, and dyadic rank aggregation. We then further move from an application-specific aspect to a general modeling aspect, where we aim to jointly exploit prior knowledge, problem structure and side information for learning low-rank modeling matrices from missing and corrupted observations. Such a modeling approach provides a general treatment to better model dyadic interactions in various machine learning applications. More importantly, we provide comprehensive theoretical analyses and performance guarantees to help us understand the utility of the additional information and quantify the merits of the proposed methods. These results therefore demonstrate the effectiveness of proposed approaches under both practical and theoretical aspects.
Advisors/Committee Members: Dhillon, Inderjit S. (advisor), Grauman, Kristen (committee member), Niekum, Scott (committee member), Hsieh, Cho-Jui (committee member).
Subjects/Keywords: Dyadic interaction modeling; Statistical machine learning; Signed network analysis; Signed graph clustering; Dyadic rank aggregation; Matrix completion; Robust PCA; Side information
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Chiang, K. (2017). Statistical analysis for modeling dyadic interactions using machine learning methods. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/47368
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):
Chiang, Kai-Yang. “Statistical analysis for modeling dyadic interactions using machine learning methods.” 2017. Thesis, University of Texas – Austin. Accessed February 16, 2019.
http://hdl.handle.net/2152/47368.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Chiang, Kai-Yang. “Statistical analysis for modeling dyadic interactions using machine learning methods.” 2017. Web. 16 Feb 2019.
Vancouver:
Chiang K. Statistical analysis for modeling dyadic interactions using machine learning methods. [Internet] [Thesis]. University of Texas – Austin; 2017. [cited 2019 Feb 16].
Available from: http://hdl.handle.net/2152/47368.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Chiang K. Statistical analysis for modeling dyadic interactions using machine learning methods. [Thesis]. University of Texas – Austin; 2017. Available from: http://hdl.handle.net/2152/47368
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
University of Texas – Austin
4.
-6763-2625.
Multilayered skill learning and movement coordination for autonomous robotic agents.
Degree: Computer Sciences, 2017, University of Texas – Austin
URL: http://hdl.handle.net/2152/62889
► 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)
▼ 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 in both industrial and domestic settings. An increase in the number of robots, and the likely subsequent decrease in the ratio of people currently trained to directly control the robots, engenders a need for robots to be able to act autonomously. Larger numbers of robots present together provide new challenges and opportunities for developing complex autonomous robot behaviors capable of multirobot collaboration and coordination.
The focus of this thesis is twofold. The first part explores applying machine learning techniques to teach simulated humanoid robots skills such as how to move or walk and manipulate objects in their environment. Learning is performed using reinforcement learning policy search methods, and layered learning methodologies are employed during the learning process in which multiple lower level skills are incrementally learned and combined with each other to develop richer higher level skills. By incrementally learning skills in layers such that new skills are learned in the presence of previously learned skills, as opposed to individually in isolation, we ensure that the learned skills will work well together and can be combined to perform complex behaviors (e.g. playing soccer). The second part of the thesis centers on developing algorithms to coordinate the movement and efforts of multiple robots working together to quickly complete tasks. These algorithms prioritize minimizing the makespan, or time for all robots to complete a task, while also attempting to avoid interference and collisions among the robots. An underlying objective of this research is to develop techniques and methodologies that allow autonomous robots to robustly interact with their environment (through skill learning) and with each other (through movement coordination) in order to perform tasks and accomplish goals asked of them.
The work in this thesis is implemented and evaluated in the RoboCup 3D simulation soccer domain, and has been a key component of the UT
Austin Villa team winning the RoboCup 3D simulation league world championship six out of the past seven years.
Advisors/Committee Members: Stone, Peter, 1971- (advisor), Ballard, Dana (committee member), Egerstedt, Magnus (committee member), Miikkulainen, Risto (committee member), Niekum, Scott (committee member).
Subjects/Keywords: Overlapping layered learning; Role assignment; Reinforcement learning; Robotics; Robot soccer
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
-6763-2625. (2017). Multilayered skill learning and movement coordination for autonomous robotic agents. (Thesis). 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
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
-6763-2625. “Multilayered skill learning and movement coordination for autonomous robotic agents.” 2017. Thesis, University of Texas – Austin. Accessed February 16, 2019.
http://hdl.handle.net/2152/62889.
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
-6763-2625. “Multilayered skill learning and movement coordination for autonomous robotic agents.” 2017. Web. 16 Feb 2019.
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] [Thesis]. University of Texas – Austin; 2017. [cited 2019 Feb 16].
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
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
-6763-2625. Multilayered skill learning and movement coordination for autonomous robotic agents. [Thesis]. University of Texas – Austin; 2017. 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
Not specified: Masters Thesis or Doctoral Dissertation
University of Texas – Austin
5.
Jain, Suyog Dutt.
Human machine collaboration for foreground segmentation in images and videos.
Degree: Computer Sciences, 2018, University of Texas – Austin
URL: http://hdl.handle.net/2152/63453
► Foreground segmentation is defined as the problem of generating pixel level foreground masks for all the objects in a given image or video. Accurate foreground…
(more)
▼ Foreground segmentation is defined as the problem of generating pixel level foreground masks for all the objects in a given image or video. Accurate foreground segmentations in images and videos have several potential applications such as improving search, training richer object detectors, image synthesis and re-targeting, scene and activity understanding, video summarization, and post-production video editing.
One effective way to solve this problem is human-machine collaboration. The main idea is to let humans guide the segmentation process through some partial supervision. As humans, we are extremely good at perception and can easily identify the foreground regions. Computers, on the other hand, lack this capability, but are extremely good at continuously processing large volumes of data at the lowest level of detail with great efficiency. Bringing these complementary strengths together can lead to systems which are accurate and cost-effective at the same time. However, in any such human-machine collaboration system, cost effectiveness and higher accuracy are competing goals. While more involvement from humans can certainly lead to higher accuracy, it also leads to increased cost both in terms of time and money. On the other hand, relying more on machines is cost-effective, but algorithms are still nowhere near human-level performance. Balancing this cost versus accuracy trade-off holds the key behind success for such a hybrid system.
In this thesis, I develop foreground segmentation algorithms which effectively and efficiently make use of human guidance for accurately segmenting foreground objects in images and videos. The algorithms developed in this thesis actively reason about the best modalities or interactions through which a user can provide guidance to the system for generating accurate segmentations. At the same time, these algorithms are also capable of prioritizing human guidance on instances where it is most needed. Finally, when structural similarity exists within data (e.g., adjacent frames in a video or similar images in a collection), the algorithms developed in this thesis are capable of propagating information from instances which have received human guidance to the ones which did not. Together, these characteristics result in a substantial savings in human annotation cost while generating high quality foreground segmentations in images and videos.
In this thesis, I consider three categories of segmentation problems all of which can greatly benefit from human-machine collaboration. First, I consider the problem of interactive image segmentation. In traditional interactive methods a human annotator provides a coarse spatial annotation (e.g., bounding box or freehand outlines) around the object of interest to obtain a segmentation. The mode of manual annotation used affects both its accuracy and ease-of-use. Whereas existing methods assume a fixed form of input no matter the image, in this thesis I propose a data-driven algorithm which learns whether an interactive segmentation method will…
Advisors/Committee Members: Grauman, Kristen Lorraine, 1979- (advisor), Mooney, Raymond (committee member), Corso, Jason (committee member), Niekum, Scott (committee member), Vouga, Paul Etienne (committee member).
Subjects/Keywords: Computer vision; Crowdsourcing; Human machine collaboration; Image and video segmentation; Image segmentation; Video segmentation; Foreground segmentation; Object segmentation
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Jain, S. D. (2018). Human machine collaboration for foreground segmentation in images and videos. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/63453
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):
Jain, Suyog Dutt. “Human machine collaboration for foreground segmentation in images and videos.” 2018. Thesis, University of Texas – Austin. Accessed February 16, 2019.
http://hdl.handle.net/2152/63453.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Jain, Suyog Dutt. “Human machine collaboration for foreground segmentation in images and videos.” 2018. Web. 16 Feb 2019.
Vancouver:
Jain SD. Human machine collaboration for foreground segmentation in images and videos. [Internet] [Thesis]. University of Texas – Austin; 2018. [cited 2019 Feb 16].
Available from: http://hdl.handle.net/2152/63453.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Jain SD. Human machine collaboration for foreground segmentation in images and videos. [Thesis]. University of Texas – Austin; 2018. Available from: http://hdl.handle.net/2152/63453
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
University of Texas – Austin
6.
-6888-3095.
Embodied learning for visual recognition.
Degree: Electrical and Computer Engineering, 2017, University of Texas – Austin
URL: http://hdl.handle.net/2152/63489
► The field of visual recognition in recent years has come to rely on large expensively curated and manually labeled "bags of disembodied images". In the…
(more)
▼ The field of visual recognition in recent years has come to rely on large expensively curated and manually labeled "bags of disembodied images". In the wake of this, my focus has been on understanding and exploiting alternate "free" sources of supervision available to visual learning agents that are situated within real environments. For example, even simply moving from orderless image collections to continuous visual observations offers opportunities to understand the dynamics and other physical properties of the visual world. Further, embodied agents may have the abilities to move around their environment and/or effect changes within it, in which case these abilities offer new means to acquire useful supervision. In this dissertation, I present my work along this and related directions.
Advisors/Committee Members: Grauman, Kristen Lorraine, 1979- (advisor), Efros, Alexei (committee member), Ghosh, Joydeep (committee member), Niekum, Scott (committee member), Thomaz, Andrea (committee member).
Subjects/Keywords: Computer vision; Unsupervised learning; Embodied learning
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
-6888-3095. (2017). Embodied learning for visual recognition. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/63489
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
-6888-3095. “Embodied learning for visual recognition.” 2017. Thesis, University of Texas – Austin. Accessed February 16, 2019.
http://hdl.handle.net/2152/63489.
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
-6888-3095. “Embodied learning for visual recognition.” 2017. Web. 16 Feb 2019.
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Vancouver:
-6888-3095. Embodied learning for visual recognition. [Internet] [Thesis]. University of Texas – Austin; 2017. [cited 2019 Feb 16].
Available from: http://hdl.handle.net/2152/63489.
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
-6888-3095. Embodied learning for visual recognition. [Thesis]. University of Texas – Austin; 2017. Available from: http://hdl.handle.net/2152/63489
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Not specified: Masters Thesis or Doctoral Dissertation
7.
-8188-7093.
A deep learning framework for model-free 6 degree of freedom object tracking.
Degree: Computer Sciences, 2017, University of Texas – Austin
URL: http://hdl.handle.net/2152/60458
► In this work we address the challenging task of 6 degree of freedom (DoF), model-free object tracking. We propose a new deep learning framework that…
(more)
▼ In this work we address the challenging task of 6 degree of freedom (DoF), model-free object tracking. We propose a new deep learning framework that explores the merit of using weakly supervised semantic segmentation as part of the object tracking pipeline. Our framework approaches the task by considering object poses in their 7D representation: a 3D vector to represent position and a unit quaternion to represent orientation. We present a novel CNN architecture, coined VGGSibs, used to regress the predicted pose. We collect a data set of several common items and evaluate our framework on both a test set withheld from our training data and on an “in the wild” set collected in a significantly different environment. Our approach achieves an average error of 7.92 cm and 21.98 degrees on the test set and 21.83 cm and 83.79 degrees on in the wild data, demonstrating that our framework generalizes reasonably well to test data that is from a similar distribution as the training data. In ablation experiments, we test our framework without the use of segmentation as a baseline. Our full framework outperforms the baseline significantly on in the wild data, thus demonstrating that the use of semantic segmentation improves the generalization performance of the framework when deployed in new environments.
Advisors/Committee Members: Niekum, Scott David (advisor), Kraehenbuehl, Philipp (committee member).
Subjects/Keywords: Object-tracking; 6 DoF; Deep learning; Model-free; Pose prediction
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
-8188-7093. (2017). A deep learning framework for model-free 6 degree of freedom object tracking. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/60458
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
-8188-7093. “A deep learning framework for model-free 6 degree of freedom object tracking.” 2017. Thesis, University of Texas – Austin. Accessed February 16, 2019.
http://hdl.handle.net/2152/60458.
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
-8188-7093. “A deep learning framework for model-free 6 degree of freedom object tracking.” 2017. Web. 16 Feb 2019.
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Vancouver:
-8188-7093. A deep learning framework for model-free 6 degree of freedom object tracking. [Internet] [Thesis]. University of Texas – Austin; 2017. [cited 2019 Feb 16].
Available from: http://hdl.handle.net/2152/60458.
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
-8188-7093. A deep learning framework for model-free 6 degree of freedom object tracking. [Thesis]. University of Texas – Austin; 2017. Available from: http://hdl.handle.net/2152/60458
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Not specified: Masters Thesis or Doctoral Dissertation
8.
Khandelwal, Piyush.
On-demand coordination of multiple service robots.
Degree: Computer Sciences, 2017, University of Texas – Austin
URL: http://hdl.handle.net/2152/61382
► 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)
▼ 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 mobile robots may be available in the environment performing routine duties such as cleaning, building maintenance, or patrolling, and that each robot may have a set of basic interfaces and manipulation tools to interact with one another as well as humans in the environment, is it possible to coordinate multiple robots for a previously unplanned on-demand task? The research presented in this dissertation aims to begin answering this question.
This dissertation makes three main contributions. The first contribution of this work is a formal framework for coordinating multiple robots to perform an on-demand task while balancing two objectives: (i) complete this on-demand task as quickly as possible, and (ii) minimize the total amount of time each robot is diverted from its routine duties. We formalize this stochastic sequential decision making problem, termed on-demand multi-robot coordination, as a Markov decision Process (MDP). Furthermore, we study this problem in the context of a specific on-demand task called multi-robot human guidance, where multiple robots need to coordinate and efficiently guide a visitor to his destination.
Second, we develop and analyze stochastic planning algorithms, in order to efficiently solve the on-demand multi-robot coordination problem in real-time. Monte Carlo Tree Search (MCTS) planning algorithms have demonstrated excellent results solving MDPs with large state-spaces and high action branching. We propose variants to the MCTS algorithm that use biased backpropagation techniques for value estimation, which can help MCTS converge to reasonable yet suboptimal policies quickly when compared to standard unbiased Monte Carlo backpropagation. In addition to using these planning algorithms for efficiently solving the on-demand multi-robot coordination problem in real-time, we also analyze their performance using benchmark domains from the International Planning Competition (IPC).
The third and final contribution of this work is the development of a multi-robot system built on top of the Segway RMP platform at the Learning Agents Research Group, UT
Austin, and the implementation and evaluation of the on-demand multi-robot coordination problem and two different planning algorithm on this platform. We also perform two studies using simulated environments, where real humans control a simulated avatar, to test the implementation of the MDP formalization and planning algorithms presented in this dissertation.
Advisors/Committee Members: Stone, Peter, 1971- (advisor), Grauman, Kristen (committee member), Niekum, Scott (committee member), Thomaz, Andrea (committee member), Veloso, Manuela (committee member).
Subjects/Keywords: Multi-robot coordination; Monte Carlo tree search; Markov decision processes; Probabilistic planning; Multi-robot systems
Record Details
Similar Records
Cite
Share »
Record Details
Similar Records
Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Khandelwal, P. (2017). On-demand coordination of multiple service robots. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/61382
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):
Khandelwal, Piyush. “On-demand coordination of multiple service robots.” 2017. Thesis, University of Texas – Austin. Accessed February 16, 2019.
http://hdl.handle.net/2152/61382.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Khandelwal, Piyush. “On-demand coordination of multiple service robots.” 2017. Web. 16 Feb 2019.
Vancouver:
Khandelwal P. On-demand coordination of multiple service robots. [Internet] [Thesis]. University of Texas – Austin; 2017. [cited 2019 Feb 16].
Available from: http://hdl.handle.net/2152/61382.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Khandelwal P. On-demand coordination of multiple service robots. [Thesis]. University of Texas – Austin; 2017. Available from: http://hdl.handle.net/2152/61382
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
. 
Open Access Theses and Dissertations
