Multilayered skill learning and movement coordination for autonomous robotic agents

With advances in technology expanding the capabilities of robots, while at the same time making robots cheaper to manufacture, robots are rapidly becoming more prevalent in both industrial and domestic settings. An increase in the number of robots, and the likely subsequent decrease in the ratio of people currently trained to directly control the robots, engenders a need for robots to be able to act autonomously. Larger numbers of robots present together provide new challenges and opportunities for developing complex autonomous robot behaviors capable of multirobot collaboration and coordination. The focus of this thesis is twofold. The first part explores applying machine learning techniques to teach simulated humanoid robots skills such as how to move or walk and manipulate objects in their environment. Learning is performed using reinforcement learning policy search methods, and layered learning methodologies are employed during the learning process in which multiple lower level skills are incrementally learned and combined with each other to develop richer higher level skills. By incrementally learning skills in layers such that new skills are learned in the presence of previously learned skills, as opposed to individually in isolation, we ensure that the learned skills will work well together and can be combined to perform complex behaviors (e.g. playing soccer). The second part of the thesis centers on developing algorithms to coordinate the movement and efforts of multiple robots working together to quickly complete tasks. These algorithms prioritize minimizing the makespan, or time for all robots to complete a task, while also attempting to avoid interference and collisions among the robots. An underlying objective of this research is to develop techniques and methodologies that allow autonomous robots to robustly interact with their environment (through skill learning) and with each other (through movement coordination) in order to perform tasks and accomplish goals asked of them. The work in this thesis is implemented and evaluated in the RoboCup 3D simulation soccer domain, and has been a key component of the UT Austin Villa team winning the RoboCup 3D simulation league world championship six out of the past seven years.Computer Science

Application of Fuzzy State Aggregation and Policy Hill Climbing to Multi-Agent Systems in Stochastic Environments

Reinforcement learning is one of the more attractive machine learning technologies, due to its unsupervised learning structure and ability to continually even as the operating environment changes. Applying this learning to multiple cooperative software agents (a multi-agent system) not only allows each individual agent to learn from its own experience, but also opens up the opportunity for the individual agents to learn from the other agents in the system, thus accelerating the rate of learning. This research presents the novel use of fuzzy state aggregation, as the means of function approximation, combined with the policy hill climbing methods of Win or Lose Fast (WoLF) and policy-dynamics based WoLF (PD-WoLF). The combination of fast policy hill climbing (PHC) and fuzzy state aggregation (FSA) function approximation is tested in two stochastic environments; Tileworld and the robot soccer domain, RoboCup. The Tileworld results demonstrate that a single agent using the combination of FSA and PHC learns quicker and performs better than combined fuzzy state aggregation and Q-learning lone. Results from the RoboCup domain again illustrate that the policy hill climbing algorithms perform better than Q-learning alone in a multi-agent environment. The learning is further enhanced by allowing the agents to share their experience through a weighted strategy sharing

Natural user interfaces for interdisciplinary design review using the Microsoft Kinect

As markets demand engineered products faster, waiting on the cyclical design processes of the past is not an option. Instead, industry is turning to concurrent design and interdisciplinary teams. When these teams collaborate, engineering CAD tools play a vital role in conceptualizing and validating designs. These tools require significant user investment to master, due to challenging interfaces and an overabundance of features. These challenges often prohibit team members from using these tools for exploring designs. This work presents a method allowing users to interact with a design using intuitive gestures and head tracking, all while keeping the model in a CAD format. Specifically, Siemens\u27 Teamcenter® Lifecycle Visualization Mockup (Mockup) was used to display design geometry while modifications were made through a set of gestures captured by a Microsoft KinectTM in real time. This proof of concept program allowed a user to rotate the scene, activate Mockup\u27s immersive menu, move the immersive wand, and manipulate the view based on head position. This work also evaluates gesture usability and task completion time for this proof of concept system. A cognitive model evaluation method was used to evaluate the premise that gesture-based user interfaces are easier to use and learn with regards to time than a traditional mouse and keyboard interface. Using a cognitive model analysis tool allowed the rapid testing of interaction concepts without the significant overhead of user studies and full development cycles. The analysis demonstrated that using the KinectTM is a feasible interaction mode for CAD/CAE programs. In addition, the analysis pointed out limitations in the gesture interfaces ability to compete time wise with easily accessible customizable menu options

Virtual Reality Games for Motor Rehabilitation

This paper presents a fuzzy logic based method to track user satisfaction without the need for devices to monitor users physiological conditions. User satisfaction is the key to any product’s acceptance; computer applications and video games provide a unique opportunity to provide a tailored environment for each user to better suit their needs. We have implemented a non-adaptive fuzzy logic model of emotion, based on the emotional component of the Fuzzy Logic Adaptive Model of Emotion (FLAME) proposed by El-Nasr, to estimate player emotion in UnrealTournament 2004. In this paper we describe the implementation of this system and present the results of one of several play tests. Our research contradicts the current literature that suggests physiological measurements are needed. We show that it is possible to use a software only method to estimate user emotion

Seven Dimensions of Wellness in Athletes with Upper Extremity Orthopedic Injuries: A Manual for Occupational Therapists

Increased attention has been placed on sports and athletes within the United States cultural. The latest reports of the noted sports economist Andrew Zimbalist estimated that the annual revenue of only 4 North American sports amounts to approximately $15 billion (Markovits, 2010). Athletes with upper extremity orthopedic injuries have traditionally been treated by occupational hand therapists (Hanson, Nabavi, & Yuen, 2000) however; research does not adequately address their needs outside of physical injury and musculoskeletal gain. Evidence is lacking within the profession of occupational therapy that encompasses the athlete as an occupational being. An occupational being is a person who participates in everyday occupations as an essential aspect of life. The physical dimension of wellness has traditionally been at the forefront of occupational hand therapy neglecting all other areas of wellness (social, spiritual, environmental, occupational, emotional, and intellectual dimensions of wellness). The Seven Dimensions of Wellness with Upper Extremity Orthopedic Injuries: A Manual for Occupational Therapists will provide occupational hand therapists with a resource to achieve successful outcomes with a client-centered Occupational Adaptation approach within a hand therapy facility. The manual was developed to guide intended to be used as a guide for therapists to implement at the beginning (initial evaluation), during (intervention), and at the end of therapy (discharge planning) to direct the client and therapist through a process of adaptation (rehabilitation) involving the Seven Dimensions of Wellness. The manual includes case studies, activities, and possible interventions associated with each dimension of wellness. The step-by-step instructions enable the therapist and the athlete to collaborate the treatment direction based on a combination of the athlete\u27s concerns paired with the occupational therapists‟ professional knowledge. It is hypothesized that athlete satisfaction and wellness recovery will be increased in treatment of athletes through guidance from this manual