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

The Daily Texan

Texas Student Medi

The Daily Texan

Texas Student Medi

The Daily Texan

Texas Student Medi

Development of humanoid robot Aldebaran NAO's behaviour logic for soccer software

Aldebaran Roboticsi arendatud NAO humanoidrobotit kasutatakse jalgpallurina RoboCup võistlusel Standard Platform League, kus kõik robotid on sama riistvaraga ja erinevad ainult tarkvara poolest. RoboCup võistluse eesmärk on populariseerida robootikat ja intellektitehnikat. Käesoleva bakalaureusetöö eesmärk oli arendada välja RoboCup SPL 2014. aastal toimuva võistluse nõuetele vastav jalgpallitarkvara käitumisloogika, mis põhineb Texase Ülikooli võistkonna UT Austin Villa 2012. aastal avalikustatud koodil. Töö käigus uuriti Austin Villa koodi ja teiste meeskondade lahendusi, sooritati testid roboti vastupidavuse ja objektituvastuse piiride teada saamiseks ning loodi 2014. aasta võistluse reeglitele vastav käitumisstrateegia, mida on robotitel kasulik kasutada siis, kui robotitevaheline ühendus on katkenud. Loodud strateegias on robotid jagatud tsoonidesse ning kui pall on roboti tsoonis, siis lüüakse see vastase värava suunas. Kui pall ei ole mängija tsoonis, siis liigub ta vastavalt palli asukohale kindlaks määratud staatilistesse punktidesse väljakul. Töö valmis koostöös Philosopheri meeskonnaga, kes osaleb juulis 2014 Brasiilias toimuval RoboCup võistlusel. Vastavalt võistkonna eesmärkidele propageeriti robootikat Eestis ning sooritati demonstratsioone Robotexil 2013, FIRST® LEGO® League Eesti ja Läti poolfinaalis 2013 ja RoboMiku Lahingus 2014. Töö lõpus pakuti välja lahenduse idee, kuidas loodud individuaalstrateegiat muuta meeskondlikuks strateegiaks

RoboCup 2D Soccer Simulation League: Evaluation Challenges

We summarise the results of RoboCup 2D Soccer Simulation League in 2016 (Leipzig), including the main competition and the evaluation round. The evaluation round held in Leipzig confirmed the strength of RoboCup-2015 champion (WrightEagle, i.e. WE2015) in the League, with only eventual finalists of 2016 competition capable of defeating WE2015. An extended, post-Leipzig, round-robin tournament which included the top 8 teams of 2016, as well as WE2015, with over 1000 games played for each pair, placed WE2015 third behind the champion team (Gliders2016) and the runner-up (HELIOS2016). This establishes WE2015 as a stable benchmark for the 2D Simulation League. We then contrast two ranking methods and suggest two options for future evaluation challenges. The first one, "The Champions Simulation League", is proposed to include 6 previous champions, directly competing against each other in a round-robin tournament, with the view to systematically trace the advancements in the League. The second proposal, "The Global Challenge", is aimed to increase the realism of the environmental conditions during the simulated games, by simulating specific features of different participating countries.Comment: 12 pages, RoboCup-2017, Nagoya, Japan, July 201

Characterization of sources of radioargon in a research reactor

textOn Site Inspection is the final measure for verifying compliance of Member States with the Comprehensive Nuclear-Test-Ban Treaty. In order to enable the use of ³⁷Ar as a radiotracer for On Site Inspection, the sources of radioargon background must be characterized and quantified. A radiation transport model of the University of Texas at Austin Nuclear Engineering Teaching Laboratory (NETL) TRIGA reactor was developed to simulate the neutron flux in various regions of the reactor. An activation and depletion code was written to calculate production of ³⁷Ar in the facility based on the results of the radiation transport model. Results showed ³⁷Ar production rates of (6.567±0.31)×10² Bq·kWh⁻¹ in the re- actor pool and the air-filled irradiation facilities, and (5.811±0.40)×10⁴ Bq·kWh⁻¹ in the biological shield. Although ⁴⁰Ca activation in the biological shield was found to dominate the total radioargon inventory, the contribution to the effluent release rate would be diminished by the immobility of Ar generated in the concrete matrix and the long diffusion path of mobile radioargon. Diffusion of radioargon out of the reactor pool was found to limit the release rate but would not significantly affect the integrated release activity. The integrated ³⁷Ar release for an 8 hour operation at 950 kW was calculated to be (1.05±0.8)×10⁷ Bq, with pool emissions continuing for days and biological shield emissions continuing for tens of days following the operation. Sensitivity analyses showed that estimates for the time-dependent concentrations of ³⁷Ar in the NETL TRIGA could be made with the calculated buildup coefficients or through analytical solution of the activation equations for only (n,[gamma]) reactions in stable argon or (n,[alpha]) reactions in ⁴⁰Ca. Analyses also indicated that, for a generalized system, the integrated thermal flux can be used to calculate the buildup due to air activation and the integrated fast flux can be used to calculate the buildup due to calcium activation. Based on the results of the NETL TRIGA, an estimate of the global research reactor source term for ³⁷Ar and an estimate of ground-level ³⁷Ar concentrations near a facility were produced.Mechanical Engineerin

A Benchmark Data Set and Evaluation of Deep Learning Architectures for Ball Detection in the RoboCup SPL

This paper presents a benchmark data set for evaluating ball detection algorithms in the RoboCup Soccer Standard Platform League. We cr eated a la- belled data set of images with and without ball derived from vision log files rec- orded by multiple NAO robots in various lighting conditions. The data set con- tains 5209 labelled ball image regions and 10924 non - ball regions . Non - ball im- age region s all contain features that had been classified as a potential ball candi- date by an existing ball detector. The data set was used to train and evaluate 25 2 different Deep Convolutional Neural Network (CNN) architectures for ball de- tection. In order to control computational requirements , this evaluation focused on networks with 2 – 5 layers that could feasibly run in the vision and cognition cycle of a NAO robot using two cameras at full frame rate (2×30 Hz). The results show that the classification perfo rmance of the networks is quite insensitive to the details of the network design including input image size, number of layers and number of outputs at each layer . In an effort to reduce the computational requirements of CNNs we evaluated XNOR - Net architect ure s which quantize the weigh ts and ac tivations of a neural network to binary values . We examined XNOR - Nets corresponding to the real - valued CNNs we had already tested in or- der to quantify the effect on classification metrics. The results indicate that bal l classification performance degrad es by 12% on average when changing from real - valued CNN to corresponding XNOR - Net