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1.
Singhal, Prateek.
Multimodal tracking for robust pose estimation.
Degree: MS, Computer Science, 2016, Georgia Tech
URL: http://hdl.handle.net/1853/54970 
► An on-line 3D visual object tracking framework for monocular cameras by incorporating spatial knowledge and uncertainty from semantic mapping along with high frequency measurements from…
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▼ An on-line 3D visual object tracking framework for monocular cameras by incorporating spatial knowledge and uncertainty from semantic mapping along with high frequency measurements from visual odometry is presented. Using a combination of vision and odometry that are tightly integrated we can increase the overall performance of object based tracking for semantic mapping. We present a framework for integration of the two data-sources into a coherent framework through uncertainty based fusion/arbitration.
Advisors/Committee Members: Christensen, Henrik (advisor), Hays, James (committee member), Boots, Byron (committee member).
Subjects/Keywords: SLAM; Tracking; Vision
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APA (6th Edition):
Singhal, P. (2016). Multimodal tracking for robust pose estimation. (Masters Thesis). Georgia Tech. Retrieved from http://hdl.handle.net/1853/54970
Chicago Manual of Style (16th Edition):
Singhal, Prateek. “Multimodal tracking for robust pose estimation.” 2016. Masters Thesis, Georgia Tech. Accessed March 05, 2021.
http://hdl.handle.net/1853/54970.
MLA Handbook (7th Edition):
Singhal, Prateek. “Multimodal tracking for robust pose estimation.” 2016. Web. 05 Mar 2021.
Vancouver:
Singhal P. Multimodal tracking for robust pose estimation. [Internet] [Masters thesis]. Georgia Tech; 2016. [cited 2021 Mar 05].
Available from: http://hdl.handle.net/1853/54970.
Council of Science Editors:
Singhal P. Multimodal tracking for robust pose estimation. [Masters Thesis]. Georgia Tech; 2016. Available from: http://hdl.handle.net/1853/54970
2.
Dai, Bo.
Learning over functions, distributions and dynamics via stochastic optimization.
Degree: PhD, Computational Science and Engineering, 2018, Georgia Tech
URL: http://hdl.handle.net/1853/60316
► Machine learning has recently witnessed revolutionary success in a wide spectrum of domains. The learning objectives, model representation, and learning algorithms are important components of…
(more)
▼ Machine learning has recently witnessed revolutionary success in a wide spectrum of domains. The learning objectives, model representation, and learning algorithms are important components of machine learning methods. To construct successful machine learning methods that are naturally fit to different problems with different targets and inputs, one should consider these three components together in a principled way. This dissertation aims for developing a unified learning framework for such purpose. The heart of this framework is the optimization with the integral operator in infinite-dimensional spaces. Such an integral operator representation view in the proposed framework provides us an abstract tool to consider these three components together for plenty of machine learning tasks and will lead to efficient algorithms equipped with flexible representations achieving better approximation ability, scalability, and statistical properties. We mainly investigate several motivated machine learning problems, i.e., kernel methods, Bayesian inference, invariance learning, policy evaluation and policy optimization in reinforcement learning, as the special cases of the proposed framework with different instantiations of the integral operator in the framework. These instantiations result in the learning problems with inputs as functions, distributions, and dynamics. The corresponding algorithms are derived to handle the particular integral operators via efficient and provable stochastic approximation by exploiting the particular structure properties in the operators. The proposed framework and the derived algorithms are deeply rooted in functional analysis, stochastic optimization, nonparametric method, and Monte Carlo approximation, and contributed to several sub-fields in machine learning community, including kernel methods, Bayesian inference, and reinforcement learning. We believe the proposed framework is a valuable tool for developing machine learning methods in a principled way and can be potentially applied to many other scenarios.
Advisors/Committee Members: Zha, Hongyuan (committee member), Boots, Byron (committee member), Lan, Guanghui (committee member), Gretton, Arthur (committee member).
Subjects/Keywords: Nonparametric method; Stochastic optimization; Reproducing kernel Hilbert space (RKHS); Functional gradient; Bayesian inference; Monte-Carlo approximation; Fenchel's duality; Saddle-point problem; Reinforcement learning; Markov decision process; Bellman equation
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APA (6th Edition):
Dai, B. (2018). Learning over functions, distributions and dynamics via stochastic optimization. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/60316
Chicago Manual of Style (16th Edition):
Dai, Bo. “Learning over functions, distributions and dynamics via stochastic optimization.” 2018. Doctoral Dissertation, Georgia Tech. Accessed March 05, 2021.
http://hdl.handle.net/1853/60316.
MLA Handbook (7th Edition):
Dai, Bo. “Learning over functions, distributions and dynamics via stochastic optimization.” 2018. Web. 05 Mar 2021.
Vancouver:
Dai B. Learning over functions, distributions and dynamics via stochastic optimization. [Internet] [Doctoral dissertation]. Georgia Tech; 2018. [cited 2021 Mar 05].
Available from: http://hdl.handle.net/1853/60316.
Council of Science Editors:
Dai B. Learning over functions, distributions and dynamics via stochastic optimization. [Doctoral Dissertation]. Georgia Tech; 2018. Available from: http://hdl.handle.net/1853/60316
Georgia Tech
3.
Williams, Grady Robert.
Model predictive path integral control: Theoretical foundations and applications to autonomous driving.
Degree: PhD, Computer Science, 2019, Georgia Tech
URL: http://hdl.handle.net/1853/62666
► This thesis presents a new approach for stochastic model predictive (optimal) control: model predictive path integral control, which is based on massive parallel sampling of…
(more)
▼ This thesis presents a new approach for stochastic model predictive (optimal) control: model predictive path integral control, which is based on massive parallel sampling of control trajectories. We first show the theoretical foundations of model predictive path integral control, which are based on a combination of path integral control theory and an information theoretic interpretation of stochastic optimal control. We then apply the method to high speed autonomous driving on a 1/5 scale vehicle and analyze the performance and robustness of the method. Extensive experimental results are used to identify and solve key problems relating to robustness of the approach, which leads to a robust stochastic model predictive control algorithm capable of consistently pushing the limits of performance on the 1/5 scale vehicle.
Advisors/Committee Members: Theodorou, Evangelos A. (advisor), Rehg, James M. (committee member), Egerstedt, Magnus (committee member), Boots, Byron (committee member), Todorov, Emanuel (committee member).
Subjects/Keywords: Stochastic optimal control; Autonomous driving
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APA (6th Edition):
Williams, G. R. (2019). Model predictive path integral control: Theoretical foundations and applications to autonomous driving. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/62666
Chicago Manual of Style (16th Edition):
Williams, Grady Robert. “Model predictive path integral control: Theoretical foundations and applications to autonomous driving.” 2019. Doctoral Dissertation, Georgia Tech. Accessed March 05, 2021.
http://hdl.handle.net/1853/62666.
MLA Handbook (7th Edition):
Williams, Grady Robert. “Model predictive path integral control: Theoretical foundations and applications to autonomous driving.” 2019. Web. 05 Mar 2021.
Vancouver:
Williams GR. Model predictive path integral control: Theoretical foundations and applications to autonomous driving. [Internet] [Doctoral dissertation]. Georgia Tech; 2019. [cited 2021 Mar 05].
Available from: http://hdl.handle.net/1853/62666.
Council of Science Editors:
Williams GR. Model predictive path integral control: Theoretical foundations and applications to autonomous driving. [Doctoral Dissertation]. Georgia Tech; 2019. Available from: http://hdl.handle.net/1853/62666
Georgia Tech
4.
Cheng, Ching An.
Efficient and principled robot learning: Theory and algorithms.
Degree: PhD, Interactive Computing, 2020, Georgia Tech
URL: http://hdl.handle.net/1853/62733
► Roboticists have long envisioned fully-automated robots that can operate reliably in unstructured environments. This is an exciting but extremely difficult problem; in order to succeed,…
(more)
▼ Roboticists have long envisioned fully-automated robots that can operate reliably in unstructured environments. This is an exciting but extremely difficult problem; in order to succeed, robots must reason about sequential decisions and their consequences in face of uncertainty. As a result, in practice, the engineering effort required to build reliable robotic systems is both demanding and expensive. This research aims to provide a set of techniques for efficient and principled robot learning. We approach this challenge from a theoretical perspective that more closely integrates analysis and practical needs. These theoretical principles are applied to design better algorithms in two important aspects of robot learning: policy optimization and development of structural policies. This research uses and extends online learning, optimization, and control theory, and is demonstrated in applications including reinforcement learning, imitation learning, and structural policy fusion. A shared feature across this research is the reciprocal interaction between the development of practical algorithms and the advancement of abstract analyses. Real-world challenges force the rethinking of proper theoretical formulations, which in turn lead to refined analyses and new algorithms that can rigorously leverage these insights to achieve better performance.
Advisors/Committee Members: Boots, Byron (advisor), Gordon, Geoff (committee member), Hutchinson, Seth (committee member), Liu, Karen (committee member), Theodorou, Evangelos A. (committee member).
Subjects/Keywords: Online learning; Control theory; Robotics; Optimization; Reinforcement learning; Imitation learning
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APA (6th Edition):
Cheng, C. A. (2020). Efficient and principled robot learning: Theory and algorithms. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/62733
Chicago Manual of Style (16th Edition):
Cheng, Ching An. “Efficient and principled robot learning: Theory and algorithms.” 2020. Doctoral Dissertation, Georgia Tech. Accessed March 05, 2021.
http://hdl.handle.net/1853/62733.
MLA Handbook (7th Edition):
Cheng, Ching An. “Efficient and principled robot learning: Theory and algorithms.” 2020. Web. 05 Mar 2021.
Vancouver:
Cheng CA. Efficient and principled robot learning: Theory and algorithms. [Internet] [Doctoral dissertation]. Georgia Tech; 2020. [cited 2021 Mar 05].
Available from: http://hdl.handle.net/1853/62733.
Council of Science Editors:
Cheng CA. Efficient and principled robot learning: Theory and algorithms. [Doctoral Dissertation]. Georgia Tech; 2020. Available from: http://hdl.handle.net/1853/62733
Georgia Tech
5.
Schroecker, Yannick Karl Daniel.
Manipulating state space distributions for sample-efficient imitation-learning.
Degree: PhD, Interactive Computing, 2020, Georgia Tech
URL: http://hdl.handle.net/1853/62755
► 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)
▼ 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 or in combination with reinforcement learning, it enables agents to copy the expert's behavior and to solve complex, long-term decision making problems. However, to utilize demonstrations effectively and learn from a finite amount of data, the agent needs to develop an understanding of the environment. This thesis investigates estimators of the state-distribution gradient as a means to influence which states the agent will see and thereby guide it to imitate the expert's behavior. Furthermore, this thesis will show that approaches which reason over future states in this way are able to learn from sparse signals and thus provide a way to effectively program agents. Specifically, this dissertation aims to validate the following thesis statement:
Exploiting inherent structure in Markov chain stationary distributions allows learning agents to reason about likely future observations, and enables robust and efficient imitation learning, providing an effective and interactive way to teach agents from minimal demonstrations.
Advisors/Committee Members: Isbell, Charles L (advisor), Chernova, Sonia (committee member), Boots, Byron (committee member), Essa, Irfan (committee member), de Freitas, Nando (committee member).
Subjects/Keywords: Imitation learning; Reinforcement learning; Deep learning; Machine learning; Artificial intelligence
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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 March 05, 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. 05 Mar 2021.
Vancouver:
Schroecker YKD. Manipulating state space distributions for sample-efficient imitation-learning. [Internet] [Doctoral dissertation]. Georgia Tech; 2020. [cited 2021 Mar 05].
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
6.
You, Changxi.
Autonomous aggressive driving: theory & experiments.
Degree: PhD, Department, 2020, Georgia Tech
URL: http://hdl.handle.net/1853/62872
► Autonomous vehicles represent a major trend in future intelligent transportation systems. In order to develop autonomous vehicles, this dissertation intends to understand expert driving maneuvers…
(more)
▼ Autonomous vehicles represent a major trend in future intelligent transportation systems. In order to develop autonomous vehicles, this dissertation intends to understand expert driving maneuvers in different scenarios such as highway overtaking and off-road rally racing, which are referred to as ``aggressive'' driving in the context of this dissertation. By mimicking expert driving styles, one expects to be able to improve the vehicle's active safety and traffic efficiency in the development of autonomous vehicles. This dissertation starts from the system modeling, namely, driver modeling, vehicle modeling and traffic system modeling, for which we implement different Kalman type filters for nonlinear parameter estimation using experimental data. We then focus on the optimal decision making, path planning and control design problems for highway overtaking and off-road autonomous rally racing, respectively. We propose to use a stochastic MDP for highway traffic modeling. The new concept of ``dynamic cell'' is introduced to dynamically extract the essential state of the traffic according to different vehicle velocities, driver intents (i.e., lane-switching, braking, etc.) and sizes of the surrounding vehicles (i.e., truck, sedan, etc.). This allows us to solve the (inverse) reinforcement learning problem efficiently since the dimensionality of the state space can be maintained in a manageable level. New path planning algorithms using Bezier curves are proposed to generate everywhere 𝐶2 continuous curvature-constrained paths for highway real-time lane-switching. We demonstrate expert overtaking maneuver by implementing the proposed decision making, path planning and control algorithms on an in-house developed traffic simulator. Based on the trajectory learning result, we model high-speed cornering with a segment of steady-state cornering for off-road rally racing. We then propose a geometry-based trajectory planning algorithm using the vehicle's differential flatness. This approach avoids solving optimal control problems on-the-fly, while guaranteeing good racing performance in off-road racing.
Advisors/Committee Members: Tsiotras, Panagiotis (advisor), Feron, Eric Marie J. (committee member), Feigh, Karen (committee member), Boots, Byron (committee member), Coogan, Samuel (committee member), names.
Subjects/Keywords: Autonomous vehicle path; Path planning; System identification; Decision making; Overtaking; Rally racing
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APA (6th Edition):
You, C. (2020). Autonomous aggressive driving: theory & experiments. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/62872
Chicago Manual of Style (16th Edition):
You, Changxi. “Autonomous aggressive driving: theory & experiments.” 2020. Doctoral Dissertation, Georgia Tech. Accessed March 05, 2021.
http://hdl.handle.net/1853/62872.
MLA Handbook (7th Edition):
You, Changxi. “Autonomous aggressive driving: theory & experiments.” 2020. Web. 05 Mar 2021.
Vancouver:
You C. Autonomous aggressive driving: theory & experiments. [Internet] [Doctoral dissertation]. Georgia Tech; 2020. [cited 2021 Mar 05].
Available from: http://hdl.handle.net/1853/62872.
Council of Science Editors:
You C. Autonomous aggressive driving: theory & experiments. [Doctoral Dissertation]. Georgia Tech; 2020. Available from: http://hdl.handle.net/1853/62872
Georgia Tech
7.
Shaban, Amirreza.
Low-shot learning for object recognition, detection, and segmentation.
Degree: PhD, Interactive Computing, 2020, Georgia Tech
URL: http://hdl.handle.net/1853/63599
► Deep Neural Networks are powerful at solving classification problems in computer vision. However, learning classifiers with these models requires a large amount of labeled training…
(more)
▼ Deep Neural Networks are powerful at solving classification problems in computer vision. However, learning classifiers with these models requires a large amount of labeled training data, and recent approaches have struggled to adapt to new classes in a data-efficient manner. On the other hand, the human brain is capable of utilizing already known knowledge in order to learn new concepts with fewer examples and less supervision. Many meta-learning algorithms have been proposed to fill this gap but they come with their practical and theoretical limitations. We review the well-known bi-level optimization as a general framework for few-shot learning and hyperparameter optimization and discuss the practical limitations of computing the full gradient. We provide theoretical guarantees for the convergence of the bi-level optimization using the approximated gradients computed by the truncated back-propagation. In the next step, we propose an empirical method for few-shot semantic segmentation: instead of solving the inner optimization, we propose to directly estimate its result by a general function approximator. Finally, we will discuss extensions of this work with the focus on weakly-supervised object detection when full supervision is not available for the few training examples.
Advisors/Committee Members: Boots, Byron (advisor), Hays, James (committee member), Batra, Dhruv (committee member), Kira, Zsolt (committee member), Li, Fuxin (committee member).
Subjects/Keywords: Few-shot learning; Low-shot learning; Bi-level optimization; Few-shot semantic segmentation; Video object segmentation; Weakly-supervised few-shot object detection
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APA (6th Edition):
Shaban, A. (2020). Low-shot learning for object recognition, detection, and segmentation. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/63599
Chicago Manual of Style (16th Edition):
Shaban, Amirreza. “Low-shot learning for object recognition, detection, and segmentation.” 2020. Doctoral Dissertation, Georgia Tech. Accessed March 05, 2021.
http://hdl.handle.net/1853/63599.
MLA Handbook (7th Edition):
Shaban, Amirreza. “Low-shot learning for object recognition, detection, and segmentation.” 2020. Web. 05 Mar 2021.
Vancouver:
Shaban A. Low-shot learning for object recognition, detection, and segmentation. [Internet] [Doctoral dissertation]. Georgia Tech; 2020. [cited 2021 Mar 05].
Available from: http://hdl.handle.net/1853/63599.
Council of Science Editors:
Shaban A. Low-shot learning for object recognition, detection, and segmentation. [Doctoral Dissertation]. Georgia Tech; 2020. Available from: http://hdl.handle.net/1853/63599
Georgia Tech
8.
Yan, Xinyan.
Efficient trajectory and policy optimization using dynamics models.
Degree: PhD, Interactive Computing, 2020, Georgia Tech
URL: http://hdl.handle.net/1853/63621
► 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)
▼ 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 of physical systems is often a labor-intensive, time-consuming, and even dangerous process. This issue of data scarcity motivates us to design algorithms that benefit from prior knowledge while avoiding relying too much on domain knowledge. One general and compact form of prior knowledge is dynamics models; they summarize our knowledge of the robot in the mechanical design and prior interactions with the robot through system identification. Unfortunately, often utilizing dynamics models to their full potential is not straightforward: (1) they are computationally expensive, and (2) they can even be harmful if the model errors are not taken into account. In this thesis, we address these two issues of using dynamics models by focusing on a central problem in robotics: trajectory and policy optimization. We develop new algorithmic and theoretic foundations of (1) computationally efficient trajectory optimization and (2) unbiased sample efficient policy optimization. Our research increases the practicality of continuous-time linear dynamics models and Gaussian process dynamics models in real-time incremental trajectory optimization, and accelerates policy optimization by utilizing dynamics models for prediction and control variates while avoiding performance bias due to model errors. We evaluate our approaches on a series of robot estimation, planning, and control tasks that involve both simulated data and real robotic systems.
Advisors/Committee Members: Boots, Byron (advisor), Chernova, Sonia (committee member), Dellaert, Frank (committee member), Ratliff, Nathan (committee member), Sindhwani, Vikas (committee member).
Subjects/Keywords: Trajectory optimization; Policy optimization; State estimation; Model predictive control; Online learning; Statistical learning
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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 March 05, 2021.
http://hdl.handle.net/1853/63621.
MLA Handbook (7th Edition):
Yan, Xinyan. “Efficient trajectory and policy optimization using dynamics models.” 2020. Web. 05 Mar 2021.
Vancouver:
Yan X. Efficient trajectory and policy optimization using dynamics models. [Internet] [Doctoral dissertation]. Georgia Tech; 2020. [cited 2021 Mar 05].
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
9.
Park, Daehyung.
A multimodal execution monitor for assistive robots.
Degree: PhD, Interactive Computing, 2018, Georgia Tech
URL: http://hdl.handle.net/1853/59860
► 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)
▼ 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 wrong could help assistive robots operate more safely and effectively around people. However, the complexity of interacting with people and objects in human environments can make challenges in monitoring operations. By monitoring multimodal sensory signals, an execution monitor could perform a variety of roles, such as detecting success, determining when to switch behaviors, and otherwise exhibiting more common sense. The purpose of this dissertation is to introduce a multimodal execution monitor to improve safety and success of assistive manipulation services. To accomplish this goal, we make three main contributions. First, we introduce a data-driven anomaly detector, a part of the monitor, that reports anomalous task executions from multimodal sensory signals online. Second, we introduce a data-driven anomaly classifier that recognizes the type and cause of common anomalies through an artificial neural network after fusing multimodal features. Lastly, as the main testbed of the monitoring system, we introduce a robot-assisted feeding system for people with disabilities, using a general-purpose mobile manipulator (a PR2 robot). We evaluate the monitoring system with haptic, visual, auditory, and kinematic sensing during household tasks and human-robot interactive tasks including feeding assistance. We show multimodality improves the performance of monitoring methods by detecting and classifying a broader range of anomalies. Overall, our research demonstrates the multimodal execution monitoring system helps the assistive manipulation system to provide safe and successful assistance for people with disabilities.
Advisors/Committee Members: Kemp, Charles C. (advisor), Boots, Byron (committee member), Chernova, Sonia (committee member), Rehg, James M. (committee member), Trumbower, Randy (committee member).
Subjects/Keywords: Execution monitor; Assistive robot
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APA ·
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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 March 05, 2021.
http://hdl.handle.net/1853/59860.
MLA Handbook (7th Edition):
Park, Daehyung. “A multimodal execution monitor for assistive robots.” 2018. Web. 05 Mar 2021.
Vancouver:
Park D. A multimodal execution monitor for assistive robots. [Internet] [Doctoral dissertation]. Georgia Tech; 2018. [cited 2021 Mar 05].
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
10.
Hrolenok, Brian Paul.
Constructing and evaluating executable models of collective behavior.
Degree: PhD, Computer Science, 2018, Georgia Tech
URL: http://hdl.handle.net/1853/60751
► Multiagent simulation (MAS) can be a valuable tool for biologists and ethologists studying collective animal behavior. However, constructing models for simulation is often a time-consuming…
(more)
▼ Multiagent simulation (MAS) can be a valuable tool for biologists and ethologists studying collective animal behavior. However, constructing models for simulation is often a time-consuming manual task. Current state-of-the-art multitarget tracking algorithms can now provide high accuracy, high density tracking data of groups of research animals. Techniques from machine learning should be able to leverage the wealth of information such data provides in order to automatically find good models of collective behavior that can be executed in simulation. However, models trained using traditional single-step loss functions can lead to behaviors that are qualitatively dissimilar to the target behavior, while Expectation Maximization (EM) methods computed over full trajectories are subject to local suboptima. These problems are particularly compounded in the case of multiple interacting agents, as in collective behaviors which are the focus of this dissertation. It is useful to examine two specific categories of collective behavior: stochastic behaviors and stateful behaviors, to illustrate the need for new learning techniques and evaluation criteria. Stochastic behaviors can be captured by modeling the distribution of behavior, while models with behavioral state can capture more complex behaviors that switch between multiple low-level modes. The schooling behavior of fish, and the foraging behavior of ants provide examples through which new models and learning methods are explored, and this exploration leads naturally to a novel quantitative evaluation framework based on the statistical similarity between the observed behaviors called Behavioral Divergence. This dissertation describes methods for building and learning executable models, the trade-offs between their strengths and weaknesses, introduces a novel quantitative evaluation framework called Behavioral Divergence that complements existing approaches, and experimentally compares Behavioral Divergence with predictive performance.
Advisors/Committee Members: Balch, Tucker (advisor), Boots, Byron (committee member), Egerstedt, Magnus (committee member), Turk, Greg (committee member), Luke, Sean (committee member).
Subjects/Keywords: Executable models; Machine learning; Multiagent systems
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APA (6th Edition):
Hrolenok, B. P. (2018). Constructing and evaluating executable models of collective behavior. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/60751
Chicago Manual of Style (16th Edition):
Hrolenok, Brian Paul. “Constructing and evaluating executable models of collective behavior.” 2018. Doctoral Dissertation, Georgia Tech. Accessed March 05, 2021.
http://hdl.handle.net/1853/60751.
MLA Handbook (7th Edition):
Hrolenok, Brian Paul. “Constructing and evaluating executable models of collective behavior.” 2018. Web. 05 Mar 2021.
Vancouver:
Hrolenok BP. Constructing and evaluating executable models of collective behavior. [Internet] [Doctoral dissertation]. Georgia Tech; 2018. [cited 2021 Mar 05].
Available from: http://hdl.handle.net/1853/60751.
Council of Science Editors:
Hrolenok BP. Constructing and evaluating executable models of collective behavior. [Doctoral Dissertation]. Georgia Tech; 2018. Available from: http://hdl.handle.net/1853/60751
Georgia Tech
11.
Zafar, Munzir.
Whole body control of wheeled inverted pendulum humanoids.
Degree: PhD, Electrical and Computer Engineering, 2019, Georgia Tech
URL: http://hdl.handle.net/1853/61739
► A framework for controlling a Wheeled Inverted Pendulum (WIP) Humanoid to perform useful interactions with the environment, while dynamically balancing itself on two wheels, was…
(more)
▼ A framework for controlling a Wheeled Inverted Pendulum (WIP) Humanoid to perform useful interactions with the environment, while dynamically balancing itself on two wheels, was proposed. As humanoid platforms are characterized by several degrees of freedom, they have the ability to perform several tasks simultaneously, while obeying constraints on their motion and control. This problem is referred as Whole-Body Control in the wider humanoid literature. We develop a framework for whole-body control of WIP humanoids that can be applied directly on the physical robot, which means that it can be made robust to modeling errors. The proposed approach is hierarchical with a low level controller responsible for controlling the manipulator/body and a high-level controller that defines center of mass targets for the low-level controller to control zero dynamics of the system driving the wheels. The low-level controller plans for shorter horizons while considering more complete dynamics of the system, while the high-level controller plans for longer horizon based on an approximate model of the robot for computational efficiency.
Advisors/Committee Members: Hutchinson, Seth (advisor), Theodorou, Evangelos A. (committee member), Boots, Byron E. (committee member), Christensen, Henrik I. (committee member), Romberg, Justin (committee member).
Subjects/Keywords: Whole body control; Wheeled inverted pendulum; Humanoids; Hierarchical; Optimization; Operational space; Model predictive control
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APA (6th Edition):
Zafar, M. (2019). Whole body control of wheeled inverted pendulum humanoids. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/61739
Chicago Manual of Style (16th Edition):
Zafar, Munzir. “Whole body control of wheeled inverted pendulum humanoids.” 2019. Doctoral Dissertation, Georgia Tech. Accessed March 05, 2021.
http://hdl.handle.net/1853/61739.
MLA Handbook (7th Edition):
Zafar, Munzir. “Whole body control of wheeled inverted pendulum humanoids.” 2019. Web. 05 Mar 2021.
Vancouver:
Zafar M. Whole body control of wheeled inverted pendulum humanoids. [Internet] [Doctoral dissertation]. Georgia Tech; 2019. [cited 2021 Mar 05].
Available from: http://hdl.handle.net/1853/61739.
Council of Science Editors:
Zafar M. Whole body control of wheeled inverted pendulum humanoids. [Doctoral Dissertation]. Georgia Tech; 2019. Available from: http://hdl.handle.net/1853/61739
12.
Sawhney, Rahul.
Robust approaches and optimization for 3D data.
Degree: PhD, Interactive Computing, 2018, Georgia Tech
URL: http://hdl.handle.net/1853/61103
► We introduce a robust, purely geometric, representation framework for fundamental association and analysis problems involving multiple views and scenes. The framework utilizes surface patches /…
(more)
▼ We introduce a robust, purely geometric, representation framework for fundamental association and analysis problems involving multiple views and scenes. The framework utilizes surface patches / segments as the underlying data unit, and is capable of effectively harnessing macro scale 3D geometry in real world scenes. We demonstrate how this results in discriminative characterizations that are robust to high noise, local ambiguities, sharp viewpoint changes, occlusions, partially overlapping content and related challenges. We present a novel approach to find localized geometric associations between two vastly varying views of a scene, through semi-dense patch correspondences, and align them. We then present means to evaluate structural content similarity between two scenes, and to ascertain their potential association. We show how this can be utilized to obtain geometrically diverse data frame retrievals, and resultant rich, atemporal reconstructions. The presented solutions are applicable over both depth images and point cloud data. They are able to perform in settings that are significantly less restrictive than ones under which existing methods operate. In our experiments, the approaches outperformed pure 3D methods in literature. Under high variability, the approaches also compared well with solutions based on RGB and RGB-D. We then introduce a robust loss function that is generally applicable to estimation and learning problems. The loss, which is nonconvex as well as nonsmooth, is shown to have a desirable combination of theoretical properties well suited for estimation (or fitting) and outlier suppression (or rejection). In conjunction, we also present a methodology for effective optimization of a broad class of nonsmooth, nonconvex objectives – some of which would prove problematic for popular methods in literature. Promising results were obtained from our empirical analysis on 3D data. Finally, we discuss a nonparametric approach for robust mode seeking. It is based on mean shift, but does not assume homoscedastic or isotropic bandwidths. It is useful for finding modes and clustering in irregular data spaces.
Advisors/Committee Members: Isbell, Charles L. (advisor), Boots, Byron (committee member), Vela, Patricio A. (committee member), Christensen, Henrik I. (committee member), Li, Fuxin (committee member).
Subjects/Keywords: 3D; Geometry; Robust; Optimization; Association; Retrieval; Robust loss; Viewpoint invariance; RGB-D; Depth image; Point cloud; Geometric description; Matching; Correspondence; Registration; Reconstruction; Surface; Patch; Segment; Superpixel; Majorization minorization; Outlier rejection; Model fitting; Estimation; Nonlinear least absolute deviations; Mean shift; Mode seeking; Segmentation; Hierarchical; Geometric diversity; Nonsmooth; Nonconvex; Edit distance; Damerau Levenshtein; Proximal algorithms; Determinantal point process; Fisher vector; Feature space; Variational factorization; M - estimation; Structured estimation
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APA (6th Edition):
Sawhney, R. (2018). Robust approaches and optimization for 3D data. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/61103
Chicago Manual of Style (16th Edition):
Sawhney, Rahul. “Robust approaches and optimization for 3D data.” 2018. Doctoral Dissertation, Georgia Tech. Accessed March 05, 2021.
http://hdl.handle.net/1853/61103.
MLA Handbook (7th Edition):
Sawhney, Rahul. “Robust approaches and optimization for 3D data.” 2018. Web. 05 Mar 2021.
Vancouver:
Sawhney R. Robust approaches and optimization for 3D data. [Internet] [Doctoral dissertation]. Georgia Tech; 2018. [cited 2021 Mar 05].
Available from: http://hdl.handle.net/1853/61103.
Council of Science Editors:
Sawhney R. Robust approaches and optimization for 3D data. [Doctoral Dissertation]. Georgia Tech; 2018. Available from: http://hdl.handle.net/1853/61103
Georgia Tech
13.
Drews, Paul Michael.
Visual attention for high speed driving.
Degree: PhD, Electrical and Computer Engineering, 2018, Georgia Tech
URL: http://hdl.handle.net/1853/61183
► Coupling of control and perception is an especially difficult problem. This thesis investigates this problem in the context of aggressive off-road driving. By jointly developing…
(more)
▼ Coupling of control and perception is an especially difficult problem. This thesis investigates this problem in the context of aggressive off-road driving. By jointly developing a robust 1:5 scale platform and leveraging state of the art sampling based model predictive control, the problem of aggressive driving on a closed dirt track using only monocular cam- era images is addressed. It is shown that a convolutional neural network can directly learn a mapping from input images to top-down cost map. This cost map can be used by a model predictive control algorithm to drive aggressively and repeatably at the limits of grip. Further, the ability to learn an end-to-end trained attentional neural network gaze strategy is developed that allows both high performance and better generalization at our task of high speed driving. This gaze model allows us to utilize simulation data to generalize from our smaller oval track to a much more complex track setting. This gaze model is compared with that of human drivers performing the same task. Using these methods, repeatable, aggressive driving at the limits of handling using monocular camera images is shown on a physical robot.
Advisors/Committee Members: Rehg, James M. (advisor), Theodorou, Evangelos A. (committee member), Boots, Byron (committee member), Batra, Dhruv (committee member), Fox, Dieter (committee member).
Subjects/Keywords: Robotics; Computer vision; Autonomous vehicles; Neural networks; High speed
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APA (6th Edition):
Drews, P. M. (2018). Visual attention for high speed driving. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/61183
Chicago Manual of Style (16th Edition):
Drews, Paul Michael. “Visual attention for high speed driving.” 2018. Doctoral Dissertation, Georgia Tech. Accessed March 05, 2021.
http://hdl.handle.net/1853/61183.
MLA Handbook (7th Edition):
Drews, Paul Michael. “Visual attention for high speed driving.” 2018. Web. 05 Mar 2021.
Vancouver:
Drews PM. Visual attention for high speed driving. [Internet] [Doctoral dissertation]. Georgia Tech; 2018. [cited 2021 Mar 05].
Available from: http://hdl.handle.net/1853/61183.
Council of Science Editors:
Drews PM. Visual attention for high speed driving. [Doctoral Dissertation]. Georgia Tech; 2018. Available from: http://hdl.handle.net/1853/61183
Georgia Tech
14.
Vijayakumar, Ashwin Kalyan.
Improved search techniques for structured prediction.
Degree: PhD, Interactive Computing, 2020, Georgia Tech
URL: http://hdl.handle.net/1853/63701
► Many useful AI tasks like machine translation, captioning or program syn- thesis to name a few can be abstracted as structured prediction problems. For these…
(more)
▼ Many useful AI tasks like machine translation, captioning or program syn- thesis to name a few can be abstracted as structured prediction problems. For these problems, the search space is well-defined but extremely large — all English language sentences for captioning or translation and similarly, all programs that can be generated from a context-free grammar in the case of program syn- thesis. Therefore, inferring the correct output (a sentence or a program) given the input (an image or user-defined specifications) is an intractable search problem. To overcome this, heuristics — hand designed or learnt from data — are often employed. In my work, I propose modified search procedures to output multiple diverse sequences and then, for the task of outputting programs, I propose a novel search procedure that accelerates existing techniques via heuristics learnt from deep networks. Going further, I propose to study the role of memory and search i.e. process each new query with the memory of previous queries — specifically in the context of solving mathematical problems.In the context of sequence prediction tasks like image captioning or translation, I introduce Diverse Beam Search (DBS), an approximate inference technique to decode multiple relevant and diverse outputs. With the objective of producing multiple sentences that are different from each other, DBS modifies the commonly used Beam Search procedure by greedily imposing diversity constraints. In follow-up work, we directly formulate the task of modeling a set of sequences and propose a trainable search procedure dubbed diff-BS. While both algorithms are task-agnostic, image-captioning is used as the test-bed to demonstrate their effectiveness. In the context of program-synthesis, I propose Neural Guided Deductive Search (NGDS), that accelerates deductive search via learnt heuristics. We find that our approach results in a significant speedup without compromising on the quality of the solutions found. Further, I will discuss the application of this technique in the context of programming by examples and synthesis of hard problems for a given solver. Finally, I study the interplay between memory and search, specifically in the context of mathematical problem solving. Analogical reasoning is a strategy commonly adopted by humans while solving problems i.e. new and unseen problems are solved by drawing parallels to previously seen problems. Inspired by such an approach, I propose to learn suitable representations for “problems” that al- lows the reuse of solutions from previously seen problems as a building block to construct the solution for the problem at hand.
Advisors/Committee Members: Batra, Dhruv (advisor), Parikh, Devi (committee member), Boots, Byron (committee member), Jain, Prateek (committee member), Polozov, Oleksandr (committee member), Rajpurohit, Tanmay (committee member).
Subjects/Keywords: Sequence decoding; Program synthesis
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APA (6th Edition):
Vijayakumar, A. K. (2020). Improved search techniques for structured prediction. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/63701
Chicago Manual of Style (16th Edition):
Vijayakumar, Ashwin Kalyan. “Improved search techniques for structured prediction.” 2020. Doctoral Dissertation, Georgia Tech. Accessed March 05, 2021.
http://hdl.handle.net/1853/63701.
MLA Handbook (7th Edition):
Vijayakumar, Ashwin Kalyan. “Improved search techniques for structured prediction.” 2020. Web. 05 Mar 2021.
Vancouver:
Vijayakumar AK. Improved search techniques for structured prediction. [Internet] [Doctoral dissertation]. Georgia Tech; 2020. [cited 2021 Mar 05].
Available from: http://hdl.handle.net/1853/63701.
Council of Science Editors:
Vijayakumar AK. Improved search techniques for structured prediction. [Doctoral Dissertation]. Georgia Tech; 2020. Available from: http://hdl.handle.net/1853/63701
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
URL: http://hdl.handle.net/1853/64106
► 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)
▼ 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 generations of robots were limited to simple tasks in controlled environments, where only a single skill was often required. To deal with the diversity of tasks and environments associated with the real world, robots should instead have access to a library of skills. Instead of pre-programming all the desired skills, a procedure which is cumbersome and often infeasible, it is beneficial to have a framework that allows robots to acquire new skills when required. One such framework is learning from demonstration, which provides a channel for robots to learn skills from everyday users. This dissertation provides methods for learning skills from human demonstrations.
Skill learning from human demonstrations carries certain challenges. Skills can be vastly different, enforcing a range of motion constraints. Human demonstrations are also often limited in number. Lastly, generalization of learned skills can be tied to generating motions that need to satisfy additional pre-specified constraints. These constraints can be associated with feasibility, requiring motions compliant with robot's kinematics and its environment, or they may be linked to coordination, requiring correlated motions of several robot body parts. To contend with the diversity of skills, the presence of feasibility and coordination constraints, and the scarcity of data, it is beneficial to impose structure in the skill representation. The structure incorporates domain knowledge in the representation, enabling desirable generalization even when access to large amounts data is hard.
The objective of this dissertation is to develop a family of techniques that allow robots to sample-efficiently learn diverse skills from human demonstrations, and subsequently generalize the skills to novel contexts while satisfying additional constraints that may exist, concerning the feasibility and coordination of robot motions. Each proposed method comes with a structured representation, suitable for tackling the challenges associated with a subset of skills. Specifically, we present: (i) a structured multi-coordinate cost learning framework coupled with an optimization routine, that generalizes skills requiring preservation of multiple geometric properties of motions, (ii) a structured prior representation employed in a probabilistic inference framework, geared towards generating optimal and feasibility-constrained motions, (iii) a stable dynamical system representation, suitable for learning skills aimed at motions that can react instantly to dynamic perturbation, and (iv) a tree-structured stable dynamical system which synthesizes multiple dynamical system into one, and learns skills dictating feasible and coordinated, yet reactive robot motions. As a preliminary to the aforementioned learning techniques, this dissertation also provides an over-arching benchmarking effort to identify the key…
Advisors/Committee Members: Chernova, Sonia (advisor), Boots, Byron (committee member), Hutchinson, Seth (committee member), Gombolay, Matthew (committee member), Hermans, Tucker (committee member).
Subjects/Keywords: learning from demonstration; robot learning
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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 March 05, 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. 05 Mar 2021.
Vancouver:
Rana MA. Methods for Teaching Diverse Robot Skills: Leveraging Priors, Geometry, and Dynamics. [Internet] [Doctoral dissertation]. Georgia Tech; 2020. [cited 2021 Mar 05].
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
16.
Hauer, Florian M.
Path-planning algorithms in high-dimensional spaces.
Degree: PhD, Aerospace Engineering, 2019, Georgia Tech
URL: http://hdl.handle.net/1853/61206
► In this thesis, we discuss the problem of path-planning in high-dimensional spaces. Large search spaces tend to lead to slow algorithms in order to find…
(more)
▼ In this thesis, we discuss the problem of path-planning in high-dimensional spaces. Large search spaces tend to lead to slow algorithms in order to find a path or to converge towards the optimal solution of a path-planning problem. This thesis investigates both discrete and continuous search spaces. For discrete search spaces, the use of multi-scale data structure allows a planning algorithm to consider a region of space at different resolutions through the execution of the algorithm and to accelerate the execution of the algorithm. The proposed algorithm is proven to be complete, it will find a solution if one exists, or report that no solution exists. Multiple applications are presented with direct construction of the multi-scale map via perception algorithms, as well as a sampling approach for problems where constructing the multi-scale map is too expensive. For continuous search spaces, the thesis explores the use of classical optimization methods within the family of sampling-based planning algorithms. An experiment is first presented to show the convergence limits of sampling-based algorithms. Then an optimization formulation shows how samples of the search space can be repositioned in order to enhance the estimate of the value function of the problem. Finally, this optimization is integrated in the framework of Rapidly-exploring Random Trees to introduce the Deformable Rapidly-exploring Random Trees algorithm. This algorithm rapidly finds a feasible solution, similarly to the other RRT algorithms, and it also significantly increases the convergence rate of the solution thanks to the added optimization step. Analysis of the parameters and applications of the algorithm show significant improvement compared to the state-of-the-art algorithms.
Advisors/Committee Members: Tsiotras, Panagiotis (advisor), Feron, Eric (committee member), Vamvoudakis, Kyriakos (committee member), Vela, Patricio (committee member), Boots, Byron (committee member).
Subjects/Keywords: Path-planning; RRT; Sampling-based; Algorithms; Completeness; Graph; Search
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APA (6th Edition):
Hauer, F. M. (2019). Path-planning algorithms in high-dimensional spaces. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/61206
Chicago Manual of Style (16th Edition):
Hauer, Florian M. “Path-planning algorithms in high-dimensional spaces.” 2019. Doctoral Dissertation, Georgia Tech. Accessed March 05, 2021.
http://hdl.handle.net/1853/61206.
MLA Handbook (7th Edition):
Hauer, Florian M. “Path-planning algorithms in high-dimensional spaces.” 2019. Web. 05 Mar 2021.
Vancouver:
Hauer FM. Path-planning algorithms in high-dimensional spaces. [Internet] [Doctoral dissertation]. Georgia Tech; 2019. [cited 2021 Mar 05].
Available from: http://hdl.handle.net/1853/61206.
Council of Science Editors:
Hauer FM. Path-planning algorithms in high-dimensional spaces. [Doctoral Dissertation]. Georgia Tech; 2019. Available from: http://hdl.handle.net/1853/61206
17.
Dong, Jing.
Extending 3D reconstruction to temporal and multi-model sensor data for precision agriculture.
Degree: PhD, Interactive Computing, 2018, Georgia Tech
URL: http://hdl.handle.net/1853/60742
► Crop monitoring is one of the most important tasks in precision agriculture, and to reduce cost, such task is often performed autonomously by unmanned aerial…
(more)
▼ Crop monitoring is one of the most important tasks in precision agriculture, and to reduce cost, such task is often performed autonomously by unmanned aerial and ground vehicles. To capture 3D geometric information about crops, existing systems mostly use LIDAR, but LIDAR is expensive and there is a desire to replace it with cheaper sensors like monocular cameras coupled with techniques to obtain 3D reconstructions from 2D images. One of the major disadvantages of many existing 3D reconstruction algorithms is that they assume the scene is static, and they cannot be used to monitor crops growing over time. Moreover, many existing 3D reconstruction algorithms are not designed to handle multi-spectral or hyper-spectral images, which are commonly used in precision agriculture to recover information that cannot be seen by naked eye. In this work I propose a full pipeline for building 3D reconstructions from temporal and multi-modal image sequences to use in precision agriculture applications. The three major technical contributions are: (1) 3D reconstruction for low-cost systems enabled by Gaussian process based continuous-time SLAM, (2) spatio-temporal 4D reconstruction to enable the monitoring of crops over time, and (3) weakly-supervised learning of local image descriptors between multiple image modalities. I also collected a multi-year growing crop dataset in order to evaluate the performance of the proposed pipeline.
Advisors/Committee Members: Dellaert, Frank (advisor), Boots, Byron (advisor), Rehg, James (committee member), Vela, Patricio (committee member), Sinha, Sudipta (committee member).
Subjects/Keywords: Precision agriculture; SLAM; Spatio-temporal reconstruction; Weakly-supervised learning
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APA (6th Edition):
Dong, J. (2018). Extending 3D reconstruction to temporal and multi-model sensor data for precision agriculture. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/60742
Chicago Manual of Style (16th Edition):
Dong, Jing. “Extending 3D reconstruction to temporal and multi-model sensor data for precision agriculture.” 2018. Doctoral Dissertation, Georgia Tech. Accessed March 05, 2021.
http://hdl.handle.net/1853/60742.
MLA Handbook (7th Edition):
Dong, Jing. “Extending 3D reconstruction to temporal and multi-model sensor data for precision agriculture.” 2018. Web. 05 Mar 2021.
Vancouver:
Dong J. Extending 3D reconstruction to temporal and multi-model sensor data for precision agriculture. [Internet] [Doctoral dissertation]. Georgia Tech; 2018. [cited 2021 Mar 05].
Available from: http://hdl.handle.net/1853/60742.
Council of Science Editors:
Dong J. Extending 3D reconstruction to temporal and multi-model sensor data for precision agriculture. [Doctoral Dissertation]. Georgia Tech; 2018. Available from: http://hdl.handle.net/1853/60742
18.
Ji, Yangfeng.
Semantic representation learning for discourse processing.
Degree: PhD, Computer Science, 2016, Georgia Tech
URL: http://hdl.handle.net/1853/55636
► Discourse processing is to identify coherent relations, such as contrast and causal relation, from well-organized texts. The outcomes from discourse processing can benefit both research…
(more)
▼ Discourse processing is to identify coherent relations, such as contrast and causal relation, from well-organized texts. The outcomes from discourse processing can benefit both research and applications in natural language processing, such as recognizing the major opinion from a product review, or evaluating the coherence of student writings. Identifying discourse relations from texts is an essential task of discourse processing. Relation identification requires intensive semantic understanding of texts, especially when no word (e.g., but) can signal the relations. Most prior work relies on sparse representation constructed from surface-form features (including, word pairs, POS tags, etc.), which fails to encode enough semantic information. As an alternative, I propose to use distributed representations of texts, which are dense vectors and flexible enough to share information efficiently. The goal of my work is to develop new models with representation learning for discourse processing. Specifically, I present a unified framework in this thesis to be able to learn both distributed representation and discourse models jointly.The joint training not only learns the discourse models, but also helps to shape the distributed representation for the discourse models. Such that, the learned representation could encode necessary semantic information to facilitate the processing tasks. The evaluation shows that our systems outperform prior work with only surface-form representations. In this thesis, I also discuss the possibility of extending the representation learning framework into some other problems in discourse processing. The problems studied include (1) How to use representation learning to build a discourse model with only distant supervision? The investigation of this problem will help to reduce the dependency of discourse processing on the annotated data; (2) How to combine discourse processing with other NLP tasks, such as language modeling? The exploration of this problem is expected to show the value of discourse information, and draw more attention to the research of discourse processing. As the end of this thesis, it also demonstrates the benefit of using discourse information for document-level machine translation and sentiment analysis.
Advisors/Committee Members: Eisenstein, Jacob (advisor), Boots, Byron (committee member), Dyer, Chris (committee member), Riedl, Mark (committee member), Smith, Noah (committee member).
Subjects/Keywords: Semantics; Representation learning; Deep learning; Discourse; Discourse processing; Sentiment analysis
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APA ·
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APA (6th Edition):
Ji, Y. (2016). Semantic representation learning for discourse processing. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/55636
Chicago Manual of Style (16th Edition):
Ji, Yangfeng. “Semantic representation learning for discourse processing.” 2016. Doctoral Dissertation, Georgia Tech. Accessed March 05, 2021.
http://hdl.handle.net/1853/55636.
MLA Handbook (7th Edition):
Ji, Yangfeng. “Semantic representation learning for discourse processing.” 2016. Web. 05 Mar 2021.
Vancouver:
Ji Y. Semantic representation learning for discourse processing. [Internet] [Doctoral dissertation]. Georgia Tech; 2016. [cited 2021 Mar 05].
Available from: http://hdl.handle.net/1853/55636.
Council of Science Editors:
Ji Y. Semantic representation learning for discourse processing. [Doctoral Dissertation]. Georgia Tech; 2016. Available from: http://hdl.handle.net/1853/55636
19.
Mukadam, Mustafa.
Structured learning and inference for robot motion generation.
Degree: PhD, Electrical and Computer Engineering, 2019, Georgia Tech
URL: http://hdl.handle.net/1853/61714
► 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)
▼ 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 safe and feasible manner, sufficiently quickly, and in dynamic and uncertain environments. In addressing these problems, there exists a dichotomy between traditional methods and modern learning-based approaches. Often both of these paradigms exhibit complementary strengths and weaknesses, for example, while the former are interpretable and integrate prior knowledge, the latter are data-driven and flexible to design. In this thesis, I present two techniques for robot motion generation that exploit structure to bridge this gap and leverage the best of both worlds to efficiently find solutions in real-time. The first technique is a planning as inference framework that encodes structure through probabilistic graphical models, and the second technique is a reactive policy synthesis framework that encodes structure through task-map trees. Within both frameworks, I present two strategies that use said structure as a canvas to incorporate learning in a manner that is generalizable and interpretable while maintaining constraints like safety even during learning.
Advisors/Committee Members: Boots, Byron (advisor), Dellaert, Frank (committee member), Chernova, Sonia (committee member), Theodorou, Evangelos (committee member), Ratliff, Nathan (committee member).
Subjects/Keywords: Motion planning; Machine learning
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APA ·
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MLA ·
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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 March 05, 2021.
http://hdl.handle.net/1853/61714.
MLA Handbook (7th Edition):
Mukadam, Mustafa. “Structured learning and inference for robot motion generation.” 2019. Web. 05 Mar 2021.
Vancouver:
Mukadam M. Structured learning and inference for robot motion generation. [Internet] [Doctoral dissertation]. Georgia Tech; 2019. [cited 2021 Mar 05].
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
20.
Xie, Bo.
Algorithms and analysis for non-convex optimization problems in machine learning.
Degree: PhD, Computational Science and Engineering, 2017, Georgia Tech
URL: http://hdl.handle.net/1853/58642
► In this thesis, we propose efficient algorithms and provide theoretical analysis through the angle of spectral methods for some important non-convex optimization problems in machine…
(more)
▼ In this thesis, we propose efficient algorithms and provide theoretical analysis through the angle of spectral methods for some important non-convex optimization problems in machine learning. Specifically, we focus on two types of non-convex optimization problems: learning the parameters of latent variable models and learning in deep neural networks. Learning latent variable models is traditionally framed as a non-convex optimization problem through Maximum Likelihood Estimation (MLE). For some specific models such as multi-view model, we can bypass the non-convexity by leveraging the special model structure and convert the problem into spectral decomposition through Methods of Moments (MM) estimator.
In this thesis, we propose a novel algorithm that can flexibly learn a multi-view model in a non-parametric fashion. To scale the nonparametric spectral methods to large datasets, we propose an algorithm called doubly stochastic gradient descent which uses sampling to approximate two expectations in the problem, and it achieves better balance of computation and statistics by adaptively growing the model as more data arrive. Learning with neural networks is a difficult non-convex problem while simple gradient-based methods achieve great success in practice. In this part of the thesis, we try to understand the optimization landscape of learning one-hidden-layer networks with Rectified Linear (ReLU) activation functions. By directly analyzing the structure of the gradient, we can show neural networks with diverse weights have no spurious local optima. This partly explains the empirical success of gradient descent since a stationary point leads to a global optimum under diversity conditions on the neural weights.
Advisors/Committee Members: Song, Le (advisor), Vempala, Santosh (committee member), Zha, Hongyuan (committee member), Boots, Byron (committee member), Anandkumar, Animashree (committee member).
Subjects/Keywords: Machine learning; Non-convex optimization; Spectral algorithms; Neural networks; Deep learning
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APA (6th Edition):
Xie, B. (2017). Algorithms and analysis for non-convex optimization problems in machine learning. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/58642
Chicago Manual of Style (16th Edition):
Xie, Bo. “Algorithms and analysis for non-convex optimization problems in machine learning.” 2017. Doctoral Dissertation, Georgia Tech. Accessed March 05, 2021.
http://hdl.handle.net/1853/58642.
MLA Handbook (7th Edition):
Xie, Bo. “Algorithms and analysis for non-convex optimization problems in machine learning.” 2017. Web. 05 Mar 2021.
Vancouver:
Xie B. Algorithms and analysis for non-convex optimization problems in machine learning. [Internet] [Doctoral dissertation]. Georgia Tech; 2017. [cited 2021 Mar 05].
Available from: http://hdl.handle.net/1853/58642.
Council of Science Editors:
Xie B. Algorithms and analysis for non-convex optimization problems in machine learning. [Doctoral Dissertation]. Georgia Tech; 2017. Available from: http://hdl.handle.net/1853/58642
21.
Charles, Adam Shabti.
Dynamics and correlations in sparse signal acquisition.
Degree: PhD, Electrical and Computer Engineering, 2015, Georgia Tech
URL: http://hdl.handle.net/1853/53592
► One of the most important parts of engineered and biological systems is the ability to acquire and interpret information from the surrounding world accurately and…
(more)
▼ One of the most important parts of engineered and biological systems is the ability to acquire and interpret information from the surrounding world accurately and in time-scales relevant to the tasks critical to system performance. This classical concept of efficient signal acquisition has been a cornerstone of signal processing research, spawning traditional sampling theorems (e.g. Shannon-Nyquist sampling), efficient filter designs (e.g. the Parks-McClellan algorithm), novel VLSI chipsets for embedded systems, and optimal tracking algorithms (e.g. Kalman filtering). Traditional techniques have made minimal assumptions on the actual signals that were being measured and interpreted, essentially only assuming a limited bandwidth. While these assumptions have provided the foundational works in signal processing, recently the ability to collect and analyze large datasets have allowed researchers to see that many important signal classes have much more regularity than having finite bandwidth.
One of the major advances of modern signal processing is to greatly improve on classical signal processing results by leveraging more specific signal statistics. By assuming even very broad classes of signals, signal acquisition and recovery can be greatly improved in regimes where classical techniques are extremely pessimistic. One of the most successful signal assumptions that has gained popularity in recet hears is notion of sparsity. Under the sparsity assumption, the signal is assumed to be composed of a small number of atomic signals from a potentially large dictionary. This limit in the underlying degrees of freedom (the number of atoms used) as opposed to the ambient dimension of the signal has allowed for improved signal acquisition, in particular when the number of measurements is severely limited.
While techniques for leveraging sparsity have been explored extensively in many contexts, typically works in this regime concentrate on exploring static measurement systems which result in static measurements of static signals. Many systems, however, have non-trivial dynamic components, either in the measurement system's operation or in the nature of the signal being observed. Due to the promising prior work leveraging sparsity for signal acquisition and the large number of dynamical systems and signals in many important applications, it is critical to understand whether sparsity assumptions are compatible with dynamical systems. Therefore, this work seeks to understand how dynamics and sparsity can be used jointly in various aspects of signal measurement and inference.
Specifically, this work looks at three different ways that dynamical systems and sparsity assumptions can interact. In terms of measurement systems, we analyze a dynamical neural network that accumulates signal information over time. We prove a series of bounds on the length of the input signal that drives the network that can be recovered from the values at the network nodes~[1 – 9]. We also analyze sparse signals that are generated via a dynamical system…
Advisors/Committee Members: Rozell, Christopher J. (advisor), Anderson, David (committee member), Boots, Byron (committee member), Romberg, Justin K. (committee member), Davenport, Mark A. (committee member).
Subjects/Keywords: Sparsity; Dynamic filtering; Spatial filtering; Hyperspectral imagery; Compressive sensing; Echo state networks; Short-term memory; Network-based optimization
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APA (6th Edition):
Charles, A. S. (2015). Dynamics and correlations in sparse signal acquisition. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/53592
Chicago Manual of Style (16th Edition):
Charles, Adam Shabti. “Dynamics and correlations in sparse signal acquisition.” 2015. Doctoral Dissertation, Georgia Tech. Accessed March 05, 2021.
http://hdl.handle.net/1853/53592.
MLA Handbook (7th Edition):
Charles, Adam Shabti. “Dynamics and correlations in sparse signal acquisition.” 2015. Web. 05 Mar 2021.
Vancouver:
Charles AS. Dynamics and correlations in sparse signal acquisition. [Internet] [Doctoral dissertation]. Georgia Tech; 2015. [cited 2021 Mar 05].
Available from: http://hdl.handle.net/1853/53592.
Council of Science Editors:
Charles AS. Dynamics and correlations in sparse signal acquisition. [Doctoral Dissertation]. Georgia Tech; 2015. Available from: http://hdl.handle.net/1853/53592
22.
Yang, Yi.
Robust adaptation of natural language processing for language variation.
Degree: PhD, Computer Science, 2017, Georgia Tech
URL: http://hdl.handle.net/1853/58201
► Natural language processing (NLP) technology has been applied in various domains, ranging from social media and digital humanities to public health. Unfortunately, the adoption of…
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▼ Natural language processing (NLP) technology has been applied in various domains, ranging from social media and digital humanities to public health. Unfortunately, the adoption of existing NLP techniques in these areas often experiences unsatisfactory performance. Languages of new datasets and settings can be significantly different from standard NLP training corpora, and modern NLP techniques are usually vulnerable to variation in non-standard languages, in terms of the lexicon, syntax, and semantics. Previous approaches toward this problem suffer from three major weaknesses. First, they often employ supervised methods that require expensive annotations and easily become outdated with respect to the dynamic nature of languages. Second, they usually fail to leverage the valuable metadata associated with the target languages of these areas. Third, they treat language as uniform and ignore the differences in language use with respect to different individuals. In this thesis, we propose several novel techniques to overcome these weaknesses and build NLP systems that are robust to language variation. These approaches are driven by co-occurrence statistics as well as rich metadata without the need of costly annotations, and can easily adapt to new settings. First, we can transform lexical variation into text that better matches standard datasets. We present a unified unsupervised statistical model for text normalization. The relationship between standard and non-standard tokens is characterized by a log-linear model, permitting arbitrary features. Text normalization focuses on tackling variation in lexicons, and therefore improving underlying NLP tasks. Second, we can overcome language variation by adapting standard NLP tools to fit the text with variation directly. We propose a novel but simple feature embedding approach to learn joint feature representations for domain adaptation, by exploiting the feature template structure commonly used in NLP problems. We also show how to incorporate metadata attributes into feature embeddings, which helps to learn distill the domain-invariant properties of each feature over multiple related domains. Domain adaptation is able to deal with a full range of linguistic phenomenon, thus it often yields better performances than text normalization. Finally, a subtle challenge posed by variation is that language is not uniformly distributed among individuals, while traditional NLP systems usually treat texts from different authors the same. Both text normalization and domain adaptation follow the standard NLP settings and fail to handle this problem. We propose to address the difficulty by exploiting the sociological theory of it{homophily} – the tendency of socially linked individuals to behave similarly – to build models that account for language variation on an individual or a social community level. We investigate both it{label homophily} and it{linguistic homophily} to build socially adapted information extraction and sentiment analysis systems. Our work delivers…
Advisors/Committee Members: Eisenstein, Jacob (advisor), Rehg, James (committee member), Boots, Byron (committee member), Chau, Duen Horng (Polo) (committee member), Daumé III, Hal (committee member).
Subjects/Keywords: Natural language processing; Machine learning
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APA ·
Chicago ·
MLA ·
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APA (6th Edition):
Yang, Y. (2017). Robust adaptation of natural language processing for language variation. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/58201
Chicago Manual of Style (16th Edition):
Yang, Yi. “Robust adaptation of natural language processing for language variation.” 2017. Doctoral Dissertation, Georgia Tech. Accessed March 05, 2021.
http://hdl.handle.net/1853/58201.
MLA Handbook (7th Edition):
Yang, Yi. “Robust adaptation of natural language processing for language variation.” 2017. Web. 05 Mar 2021.
Vancouver:
Yang Y. Robust adaptation of natural language processing for language variation. [Internet] [Doctoral dissertation]. Georgia Tech; 2017. [cited 2021 Mar 05].
Available from: http://hdl.handle.net/1853/58201.
Council of Science Editors:
Yang Y. Robust adaptation of natural language processing for language variation. [Doctoral Dissertation]. Georgia Tech; 2017. Available from: http://hdl.handle.net/1853/58201
23.
Pan, Yunpeng.
Learning control via probabilistic trajectory optimization.
Degree: PhD, Aerospace Engineering, 2017, Georgia Tech
URL: http://hdl.handle.net/1853/59278
► A central problem in the field of robotics is to develop real-time planning and control algorithms for autonomous systems to behave intelligently under uncertainty. While…
(more)
▼ A central problem in the field of robotics is to develop real-time planning and control algorithms for autonomous systems to behave intelligently under uncertainty. While classical optimal control provides a general theoretical framework, it relies on strong assumption of full knowledge of the system dynamics and environments. Alternatively, modern reinforcement learning (RL) offers a computational framework for controlling autonomous systems with minimal prior knowledge and user intervention. However, typical RL approaches require many interactions with the physical systems, and suffer from slow convergence. Furthermore, both optimal control and RL have the difficulty of scaling to high-dimensional state and action spaces.
In order to address these challenges, we present probabilistic trajectory optimization methods for solving optimal control problems for systems with unknown or partially known dynamics. Our methods share two key characteristics: (1) we incorporate explicit uncertainty into modeling, prediction and decision making using Gaussian processes; (2) our algorithms bypass the it{curse of dimensionality} via local approximation of the value function or linearization of the Hamilton-Jacobi-Bellman (HJB) equation. Compared to related approaches, our methods offer superior combination of data efficiency and scalability. We present experimental results and comparative analyses to demonstrate the strengths of the proposed methods.
In addition, we develop fast Bayesian approximate inference methods which enable probabilistic trajectory optimizer to perform real-time receding horizon control. It can be used to train deep neural network controllers that map raw observations to actions directly. We show that our approach can be used to perform high-speed off-road autonomous driving with low-cost sensors, and without on-the-fly planning and optimization.
Advisors/Committee Members: Theodorou, Evangelos A (advisor), Boots, Byron (committee member), Johnson, Eric N (committee member), Song, Le (committee member), How, Jonathan (committee member).
Subjects/Keywords: Optimal control; Robotics; Artificial intelligence; Machine learning
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❌
APA ·
Chicago ·
MLA ·
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CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Pan, Y. (2017). Learning control via probabilistic trajectory optimization. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/59278
Chicago Manual of Style (16th Edition):
Pan, Yunpeng. “Learning control via probabilistic trajectory optimization.” 2017. Doctoral Dissertation, Georgia Tech. Accessed March 05, 2021.
http://hdl.handle.net/1853/59278.
MLA Handbook (7th Edition):
Pan, Yunpeng. “Learning control via probabilistic trajectory optimization.” 2017. Web. 05 Mar 2021.
Vancouver:
Pan Y. Learning control via probabilistic trajectory optimization. [Internet] [Doctoral dissertation]. Georgia Tech; 2017. [cited 2021 Mar 05].
Available from: http://hdl.handle.net/1853/59278.
Council of Science Editors:
Pan Y. Learning control via probabilistic trajectory optimization. [Doctoral Dissertation]. Georgia Tech; 2017. Available from: http://hdl.handle.net/1853/59278
. 
Open Access Theses and Dissertations
