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- Creators: Barrett, The Honors College
- Creators: Yang, Yezhou
- Creators: Fainekos, Georgios
- Member of: Theses and Dissertations
Description
While developing autonomous intelligent robots has been the goal of many research programs, a more practical application involving intelligent robots is the formation of teams consisting of both humans and robots. An example of such an application is search and rescue operations where robots commanded by humans are sent to environments too dangerous for humans. For such human-robot interaction, natural language is considered a good communication medium as it allows humans with less training about the robot's internal language to be able to command and interact with the robot. However, any natural language communication from the human needs to be translated to a formal language that the robot can understand. Similarly, before the robot can communicate (in natural language) with the human, it needs to formulate its communique in some formal language which then gets translated into natural language. In this paper, I develop a high level language for communication between humans and robots and demonstrate various aspects through a robotics simulation. These language constructs borrow some ideas from action execution languages and are grounded with respect to simulated human-robot interaction transcripts.
ContributorsLumpkin, Barry Thomas (Author) / Baral, Chitta (Thesis advisor) / Lee, Joohyung (Committee member) / Fainekos, Georgios (Committee member) / Arizona State University (Publisher)
Created2012
Description
As robots become more prevalent, the need is growing for efficient yet stable control systems for applications with humans in the loop. As such, it is a challenge for scientists and engineers to develop robust and agile systems that are capable of detecting instability in teleoperated systems. Despite how much research has been done to characterize the spatiotemporal parameters of human arm motions for reaching and gasping, not much has been done to characterize the behavior of human arm motion in response to control errors in a system. The scope of this investigation is to investigate human corrective actions in response to error in an anthropomorphic teleoperated robot limb. Characterizing human corrective actions contributes to the development of control strategies that are capable of mitigating potential instabilities inherent in human-machine control interfaces. Characterization of human corrective actions requires the simulation of a teleoperated anthropomorphic armature and the comparison of a human subject's arm kinematics, in response to error, against the human arm kinematics without error. This was achieved using OpenGL software to simulate a teleoperated robot arm and an NDI motion tracking system to acquire the subject's arm position and orientation. Error was intermittently and programmatically introduced to the virtual robot's joints as the subject attempted to reach for several targets located around the arm. The comparison of error free human arm kinematics to error prone human arm kinematics revealed an addition of a bell shaped velocity peak into the human subject's tangential velocity profile. The size, extent, and location of the additional velocity peak depended on target location and join angle error. Some joint angle and target location combinations do not produce an additional peak but simply maintain the end effector velocity at a low value until the target is reached. Additional joint angle error parameters and degrees of freedom are needed to continue this investigation.
ContributorsBevilacqua, Vincent Frank (Author) / Artemiadis, Panagiotis (Thesis director) / Santello, Marco (Committee member) / Trimble, Steven (Committee member) / Barrett, The Honors College (Contributor) / Mechanical and Aerospace Engineering Program (Contributor)
Created2013-05
Description
Intelli-Trail is a game where the player plays as a small blue man with the simple goal of reaching the purple door. The player will primarily interact with the game through combat. The game itself will react to the patterns in the players behavior to progressively become harder for the player to win.
ContributorsCurtin, Ethan (Author) / Gonzalez Sanchez, Javier (Thesis director) / Baron, Tyler (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
Bicycles are already used for daily transportation by a large share of the world's population and provide a partial solution for many issues facing the world today. The low environmental impact of bicycling combined with the reduced requirement for road and parking spaces makes bicycles a good choice for transportation over short distances in urban areas. Bicycle riding has also been shown to improve overall health and increase life expectancy. However, riding a bicycle may be inconvenient or impossible for persons with disabilities due to the complex and coordinated nature of the task. Automated bicycles provide an interesting area of study for human-robot interaction, due to the number of contact points between the rider and the bicycle. The goal of the Smart Bike project is to provide a platform for future study of the physical interaction between a semi-autonomous bicycle robot and a human rider, with possible applications in rehabilitation and autonomous vehicle research.
This thesis presents the development of two balance control systems, which utilize actively controlled steering and a control moment gyroscope to stabilize the bicycle at high and low speeds. These systems may also be used to introduce disturbances, which can be useful for studying human reactions. The effectiveness of the steering balance control system is verified through testing with a PID controller in an outdoor environment. Also presented is the development of a force sensitive bicycle seat which provides feedback used to estimate the pose of the rider on the bicycle. The relationship between seat force distribution is demonstrated with a motion capture experiment. A corresponding software system is developed for balance control and sensor integration, with inputs from the rider, the internal balance and steering controller, and a remote operator.
This thesis presents the development of two balance control systems, which utilize actively controlled steering and a control moment gyroscope to stabilize the bicycle at high and low speeds. These systems may also be used to introduce disturbances, which can be useful for studying human reactions. The effectiveness of the steering balance control system is verified through testing with a PID controller in an outdoor environment. Also presented is the development of a force sensitive bicycle seat which provides feedback used to estimate the pose of the rider on the bicycle. The relationship between seat force distribution is demonstrated with a motion capture experiment. A corresponding software system is developed for balance control and sensor integration, with inputs from the rider, the internal balance and steering controller, and a remote operator.
ContributorsBush, Jonathan Ernest (Author) / Zhang, Wenlong (Thesis director) / Sandy, Douglas (Committee member) / Software Engineering (Contributor, Contributor) / Engineering Programs (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
In the field of machine learning, reinforcement learning stands out for its ability to explore approaches to complex, high dimensional problems that outperform even expert humans. For robotic locomotion tasks reinforcement learning provides an approach to solving them without the need for unique controllers. In this thesis, two reinforcement learning algorithms, Deep Deterministic Policy Gradient and Group Factor Policy Search are compared based upon their performance in the bipedal walking environment provided by OpenAI gym. These algorithms are evaluated on their performance in the environment and their sample efficiency.
ContributorsMcDonald, Dax (Author) / Ben Amor, Heni (Thesis director) / Yang, Yezhou (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2018-12
Description
Deep learning and AI have grabbed tremendous attention in the last decade. The substantial accuracy improvement by neural networks in common tasks such as image classification and speech recognition has made deep learning as a replacement for many conventional machine learning techniques. Training Deep Neural networks require a lot of data, and therefore vast of amounts of computing resources to process the data and train the model for the neural network. The most obvious solution to solving this problem is to speed up the time it takes to train Deep Neural networks.
AI and deep learning workloads are different from the conventional cloud and mobile workloads, with respect to: (1) Computational Intensity, (2) I/O characteristics, and (3) communication pattern. While there is a considerable amount of research activity on the theoretical aspects of AI and Deep Learning algorithms that run with greater efficiency, there are only a few studies on the infrastructural impact of Deep Learning workloads on computing and storage resources in distributed systems.
It is typical to utilize a heterogeneous mixture of CPU and GPU devices to perform training on a neural network. Google Brain has a developed a reinforcement model that can place training operations across a heterogeneous cluster. Though it has only been tested with local devices in a single cluster. This study will explore the method’s capabilities and attempt to apply this method on a cluster with nodes across a network.
AI and deep learning workloads are different from the conventional cloud and mobile workloads, with respect to: (1) Computational Intensity, (2) I/O characteristics, and (3) communication pattern. While there is a considerable amount of research activity on the theoretical aspects of AI and Deep Learning algorithms that run with greater efficiency, there are only a few studies on the infrastructural impact of Deep Learning workloads on computing and storage resources in distributed systems.
It is typical to utilize a heterogeneous mixture of CPU and GPU devices to perform training on a neural network. Google Brain has a developed a reinforcement model that can place training operations across a heterogeneous cluster. Though it has only been tested with local devices in a single cluster. This study will explore the method’s capabilities and attempt to apply this method on a cluster with nodes across a network.
ContributorsNguyen, Andrew Hoang (Author) / Zhao, Ming (Thesis director) / Biookaghazadeh, Saman (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
Students learn in various ways \u2014 visualization, auditory, memorizing, or making analogies. Traditional lecturing in engineering courses and the learning styles of engineering students are inharmonious causing students to be at a disadvantage based on their learning style (Felder & Silverman, 1988). My study analyzes the traditional approach to learning coding skills which is unnatural to engineering students with no previous exposure and examining if visual learning enhances introductory computer science education. Visual and text-based learning are evaluated to determine how students learn introductory coding skills and associated problem solving skills. My study was conducted to observe how the two types of learning aid the students in learning how to problem solve as well as how much knowledge can be obtained in a short period of time. The application used for visual learning was Scratch and Repl.it was used for text-based learning. Two exams were made to measure the progress made by each student. The topics covered by the exam were initialization, variable reassignment, output, if statements, if else statements, nested if statements, logical operators, arrays/lists, while loop, type casting, functions, object orientation, and sorting. Analysis of the data collected in the study allow us to observe whether the traditional method of teaching programming or block-based programming is more beneficial and in what topics of introductory computer science concepts.
ContributorsVidaure, Destiny Vanessa (Author) / Meuth, Ryan (Thesis director) / Yang, Yezhou (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
As robotics technology advances, robots are being created for use in situations where they collaborate with humans on complex tasks. For this to be safe and successful, it is important to understand what causes humans to trust robots more or less during a collaborative task. This research project aims to investigate human-robot trust through a collaborative game of logic that can be played with a human and a robot together. This thesis details the development of a game of logic that could be used for this purpose. The game of logic is based upon a popular game in AI research called ‘Wumpus World’. The original Wumpus World game was a low-interactivity game to be played by humans alone. In this project, the Wumpus World game is modified for a high degree of interactivity with a human player, while also allowing the game to be played simultaneously by an AI algorithm.
ContributorsBoateng, Andrew Owusu (Author) / Sodemann, Angela (Thesis director) / Martin, Thomas (Committee member) / Software Engineering (Contributor) / Engineering Programs (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
This thesis describes a multi-robot architecture which allows teams of robots to work with humans to complete tasks. The multi-agent architecture was built using Robot Operating System and Python. This architecture was designed modularly, allowing the use of different planners and robots. The system automatically replans when robots connect or disconnect. The system was demonstrated on two real robots, a Fetch and a PeopleBot, by conducting a surveillance task on the fifth floor of the Computer Science building at Arizona State University. The next part of the system includes extensions for teaming with humans. An Android application was created to serve as the interface between the system and human teammates. This application provides a way for the system to communicate with humans in the loop. In addition, it sends location information of the human teammates to the system so that goal recognition can be performed. This goal recognition allows the generation of human-aware plans. This capability was demonstrated in a mock search and rescue scenario using the Fetch to locate a missing teammate.
ContributorsSaba, Gabriel Christer (Author) / Kambhampati, Subbarao (Thesis director) / Doupé, Adam (Committee member) / Chakraborti, Tathagata (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2017-05
Description
Bioscience High School, a small magnet high school located in Downtown Phoenix and a STEAM (Science, Technology, Engineering, Arts, Math) focused school, has been pushing to establish a computer science curriculum for all of their students from freshman to senior year. The school's Mision (Mission and Vision) is to: "..provide a rigorous, collaborative, and relevant academic program emphasizing an innovative, problem-based curriculum that develops literacy in the sciences, mathematics, and the arts, thus cultivating critical thinkers, creative problem-solvers, and compassionate citizens, who are able to thrive in our increasingly complex and technological communities." Computational thinking is an important part in developing a future problem solver Bioscience High School is looking to produce. Bioscience High School is unique in the fact that every student has a computer available for him or her to use. Therefore, it makes complete sense for the school to add computer science to their curriculum because one of the school's goals is to be able to utilize their resources to their full potential. However, the school's attempt at computer science integration falls short due to the lack of expertise amongst the math and science teachers. The lack of training and support has postponed the development of the program and they are desperately in need of someone with expertise in the field to help reboot the program. As a result, I've decided to create a course that is focused on teaching students the concepts of computational thinking and its application through Scratch and Arduino programming.
ContributorsLiu, Deming (Author) / Meuth, Ryan (Thesis director) / Nakamura, Mutsumi (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05