A framework and simulation engine for studying artificial life

The area of computer-generated artificial life-forms is a relatively recent field of inter-disciplinary study that involves mathematical modelling, physical intuition and ideas from chemistry and biology and computational science. Although the attribution of “life” to non biological systems is still controversial, several groups agree that certain emergent properties can be ascribed to computer simulated systems that can be constructed to “live” in a simulated environment. In this paper we discuss some of the issues and infrastructure necessary to construct a simulation laboratory for the study of computer generated artificial life-forms. We review possible technologies and present some preliminary studies based around simple models

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

Validity, Reliability, and Usefulness of My Jump 2 App for Measuring Vertical Jump in Primary School Children

There is a persistent need in sport science for developing a measuring tool that is affordable, portable, and easy to use. We aimed to examine the concurrent validity and test-retest reliability of the My Jump 2 app compared to a validated OptoJump instrument for measuring jump performance during the squat jump (SJ), countermovement jump (CMJ), and CMJ free arms (CMJAM) in primary school children. A total of 48 participants (11-14 years age), volunteered to participate in this research. The jumps were recorded with a validated OptoJump photoelectric cell system and a concurrent device (iPhone X through My Jump 2 app) at the same time. The participants repeated the testing procedure after two weeks to assess the reliability of the measurements (ICC). Systematic bias between sessions and tools was evaluated using the paired samples t-test and Bland and Altman analysis. High test-retest reliability (ICC > 0.89) was observed for all measures' in-between conditions. Very large correlations in the total sample were observed between the My Jump 2 app and OptoJump for SJ (r = 0.97, p = 0.001), CMJ (r = 0.97, p = 0.001), and CMJAM (r = 0.99, p = 0.001). Bland and Altman's plot depicting limits of agreement for the total sample between the OptoJump and My Jump 2 show that the majority of data points are within the 95% CIs. The results of this study suggest that My Jump 2 is a valid, reliable, and useful tool for measuring jump performance in primary school children

Algorithms for Triangles, Cones & Peaks

Three different geometric objects are at the center of this dissertation: triangles, cones and peaks. In computational geometry, triangles are the most basic shape for planar subdivisions. Particularly, Delaunay triangulations are a widely used for manifold applications in engineering, geographic information systems, telecommunication networks, etc. We present two novel parallel algorithms to construct the Delaunay triangulation of a given point set. Yao graphs are geometric spanners that connect each point of a given set to its nearest neighbor in each of $k$ cones drawn around it. They are used to aid the construction of Euclidean minimum spanning trees or in wireless networks for topology control and routing. We present the first implementation of an optimal $\mathcal{O}(n \log n)$-time sweepline algorithm to construct Yao graphs. One metric to quantify the importance of a mountain peak is its isolation. Isolation measures the distance between a peak and the closest point of higher elevation. Computing this metric from high-resolution digital elevation models (DEMs) requires efficient algorithms. We present a novel sweep-plane algorithm that can calculate the isolation of all peaks on Earth in mere minutes