Applying reinforcement learning in playing Robosoccer using the AIBO

"Robosoccer is a popular test bed for AI programs around the world in which AIBO entertainments robots take part in the middle sized soccer event. These robots need a variety of skills to perform in a semi-real environment like this. The three key challenges are manoeuvrability, image recognition and decision making skills. This research is focussed on the decision making skills ... The work focuses on whether reinforcement learning as a form of semi supervised learning can effectively contribute to the goal keeper's decision making when a shot is taken." -Master of Computing (by research

Scaled Autonomy for Networked Humanoids

Humanoid robots have been developed with the intention of aiding in environments designed for humans. As such, the control of humanoid morphology and effectiveness of human robot interaction form the two principal research issues for deploying these robots in the real world. In this thesis work, the issue of humanoid control is coupled with human robot interaction under the framework of scaled autonomy, where the human and robot exchange levels of control depending on the environment and task at hand. This scaled autonomy is approached with control algorithms for reactive stabilization of human commands and planned trajectories that encode semantically meaningful motion preferences in a sequential convex optimization framework. The control and planning algorithms have been extensively tested in the field for robustness and system verification. The RoboCup competition provides a benchmark competition for autonomous agents that are trained with a human supervisor. The kid-sized and adult-sized humanoid robots coordinate over a noisy network in a known environment with adversarial opponents, and the software and routines in this work allowed for five consecutive championships. Furthermore, the motion planning and user interfaces developed in the work have been tested in the noisy network of the DARPA Robotics Challenge (DRC) Trials and Finals in an unknown environment. Overall, the ability to extend simplified locomotion models to aid in semi-autonomous manipulation allows untrained humans to operate complex, high dimensional robots. This represents another step in the path to deploying humanoids in the real world, based on the low dimensional motion abstractions and proven performance in real world tasks like RoboCup and the DRC

Towards Agility: Definition, Benchmark and Design Considerations for Small, Quadrupedal Robots

Agile quadrupedal locomotion in animals and robots is yet to be fully understood, quantified or achieved. An intuitive notion of agility exists, but neither a concise definition nor a common benchmark can be found. Further, it is unclear, what minimal level of mechatronic complexity is needed for this particular aspect of locomotion. In this thesis we address and partially answer two primary questions: (Q1) What is agile legged locomotion (agility) and how can wemeasure it? (Q2) How can wemake agile legged locomotion with a robot a reality? To answer our first question, we define agility for robot and animal alike, building a common ground for this particular component of locomotion and introduce quantitative measures to enhance robot evaluation and comparison. The definition is based on and inspired by features of agility observed in nature, sports, and suggested in robotics related publications. Using the results of this observational and literature review, we build a novel and extendable benchmark of thirteen different tasks that implement our vision of quantitatively classifying agility. All scores are calculated from simple measures, such as time, distance, angles and characteristic geometric values for robot scaling. We normalize all unit-less scores to reach comparability between different systems. An initial implementation with available robots and real agility-dogs as baseline finalize our effort of answering the first question. Bio-inspired designs introducing and benefiting from morphological aspects present in nature allowed the generation of fast, robust and energy efficient locomotion. We use engineering tools and interdisciplinary knowledge transferred from biology to build low-cost robots able to achieve a certain level of agility and as a result of this addressing our second question. This iterative process led to a series of robots from Lynx over Cheetah-Cub-S, Cheetah-Cub-AL, and Oncilla to Serval, a compliant robot with actuated spine, high range of motion in all joints. Serval presents a high level of mobility at medium speeds. With many successfully implemented skills, using a basic kinematics-duplication from dogs (copying the foot-trajectories of real animals and replaying themotion on the robot using a mathematical interpretation), we found strengths to emphasize, weaknesses to correct and made Serval ready for future attempts to achieve even more agile locomotion. We calculated Servalâs agility scores with the result of it performing better than any of its predecessors. Our small, safe and low-cost robot is able to execute up to 6 agility tasks out of 13 with the potential to reachmore after extended development. Concluding, we like to mention that Serval is able to cope with step-downs, smooth, bumpy terrain and falling orthogonally to the ground

A Bio-inspired architecture for adaptive quadruped locomotion over irregular terrain

Tese de doutoramento Programa Doutoral em Engenharia Electrónica e de ComputadoresThis thesis presents a tentative advancement on walking control of small quadruped and humanoid position controlled robots, addressing the problem of walk generation by combining dynamical systems approach to motor control, insights from neuroethology research on vertebrate motor control and computational neuroscience. Legged locomotion is a complex dynamical process, despite the seemingly easy and natural behavior of the constantly present proficiency of legged animals. Research on locomotion and motor control in vertebrate animals from the last decades has brought to the attention of roboticists, the potential of the nature’s solutions to robot applications. Recent knowledge on the organization of complex motor generation and on mechanics and dynamics of locomotion has been successfully exploited to pursue agile robot locomotion. The work presented on this manuscript is part of an effort on the pursuit in devising a general, model free solution, for the generation of robust and adaptable walking behaviors. It strives to devise a practical solution applicable to real robots, such as the Sony’s quadruped AIBO and Robotis’ DARwIn- OP humanoid. The discussed solutions are inspired on the functional description of the vertebrate neural systems, especially on the concept of Central Pattern Generators (CPGs), their structure and organization, components and sensorimotor interactions. They use a dynamical systems approach for the implementation of the controller, especially on the use of nonlinear oscillators and exploitation of their properties. The main topics of this thesis are divided into three parts. The first part concerns quadruped locomotion, extending a previous CPG solution using nonlinear oscillators, and discussing an organization on three hierarchical levels of abstraction, sharing the purpose and knowledge of other works. It proposes a CPG solution which generates the walking motion for the whole-leg, which is then organized in a network for the production of quadrupedal gaits. The devised solution is able to produce goal-oriented locomotion and navigation as directed through highlevel commands from local planning methods. In this part, active balance on a standing quadruped is also addressed, proposing a method based on dynamical systems approach, exploring the integration of parallel postural mechanisms from several sensory modalities. The solutions are all successfully tested on the quadruped AIBO robot. In the second part, is addressed bipedal walking for humanoid robots. A CPG solution for biped walking based on the concept of motion primitives is proposed, loosely based on the idea of synergistic organization of vertebrate motor control. A set of motion primitives is shown to produce the basis of simple biped walking, and generalizable to goal-oriented walking. Using the proposed CPG, the inclusion of feedback mechanisms is investigated, for modulation and adaptation of walking, through phase transition control according to foot load information. The proposed solution is validated on the humanoid DARwIn-OP, and its application is evaluated within a whole-body control framework. The third part sidesteps a little from the other two topics. It discusses the CPG as having an alternative role to direct motor generation in locomotion, serving instead as a processor of sensory information for a feedback based motor generation. In this work a reflex based walking controller is devised for the compliant quadruped Oncilla robot, to serve as purely feedback based walking generation. The capabilities of the reflex network are shown in simulations, followed by a brief discussion on its limitations, and how they could be improved by the inclusion of a CPG.Esta tese apresenta uma tentativa de avanço no controlo de locomoção para pequenos robôs quadrúpedes e bipedes controlados por posição, endereçando o problema de geração motora através da combinação da abordagem de sistemas dinâmicos para o controlo motor, e perspectivas de investigação neuroetologia no controlo motor vertebrado e neurociência computacional. Andar é um processo dinâmico e complexo, apesar de parecer um comportamento fácil e natural devido à presença constante de animais proficientes em locomoção terrestre. Investigação na área da locomoção e controlo motor em animais vertebrados nas últimas decadas, trouxe à atenção dos roboticistas o potencial das soluções encontradas pela natureza aplicadas a aplicações robóticas. Conhecimento recente relativo à geração de comportamentos motores complexos e da mecânica da locomoção tem sido explorada com sucesso na procura de locomoção ágil na robótica. O trabalho apresentado neste documento é parte de um esforço no desenho de uma solução geral, e independente de modelos, para a geração robusta e adaptável de comportamentos locomotores. O foco é desenhar uma solução prática, aplicável a robôs reais, tal como o quadrúpede Sony AIBO e o humanóide DARwIn-OP. As soluções discutidas são inspiradas na descrição funcional do sistema nervoso vertebrado, especialmente no conceito de Central Pattern Generators (CPGs), a sua estrutura e organização, componentes e interacção sensorimotora. Estas soluções são implementadas usando uma abordagem em sistemas dinâmicos, focandos o uso de osciladores não lineares e a explorando as suas propriedades. Os tópicos principais desta tese estão divididos em três partes. A primeira parte explora o tema de locomoção quadrúpede, expandindo soluções prévias de CPGs usando osciladores não lineares, e discutindo uma organização em três níveis de abstracção, partilhando as ideias de outros trabalhos. Propõe uma solução de CPG que gera os movimentos locomotores para uma perna, que é depois organizado numa rede, para a produção de marcha quadrúpede. A solução concebida é capaz de produzir locomoção e navegação, comandada através de comandos de alto nível, produzidos por métodos de planeamento local. Nesta parte também endereçado o problema da manutenção do equilíbrio num robô quadrúpede parado, propondo um método baseado na abordagem em sistemas dinâmicos, explorando a integração de mecanismos posturais em paralelo, provenientes de várias modalidades sensoriais. As soluções são todas testadas com sucesso no robô quadrupede AIBO. Na segunda parte é endereçado o problema de locomoção bípede. É proposto um CPG baseado no conceito de motion primitives, baseadas na ideia de uma organização sinergética do controlo motor vertebrado. Um conjunto de motion primitives é usado para produzir a base de uma locomoção bípede simples e generalizável para navegação. Esta proposta de CPG é usada para de seguida se investigar a inclusão de mecanismos de feedback para modulação e adaptação da marcha, através do controlo de transições entre fases, de acordo com a informação de carga dos pés. A solução proposta é validada no robô humanóide DARwIn-OP, e a sua aplicação no contexto do framework de whole-body control é também avaliada. A terceira parte desvia um pouco dos outros dois tópicos. Discute o CPG como tendo um papel alternativo ao controlo motor directo, servindo em vez como um processador de informação sensorial para um mecanismo de locomoção puramente em feedback. Neste trabalho é desenhado um controlador baseado em reflexos para a geração da marcha de um quadrúpede compliant. As suas capacidades são demonstradas em simulação, seguidas por uma breve discussão nas suas limitações, e como estas podem ser ultrapassadas pela inclusão de um CPG.The presented work was possible thanks to the support by the Portuguese Science and Technology Foundation through the PhD grant SFRH/BD/62047/2009

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

Learning Motion Skills for a Humanoid Robot

This thesis investigates the learning of motion skills for humanoid robots. As groundwork, a humanoid robot with integrated fall management was developed as an experimental platform. Then, two different approaches for creating motion skills were investigated. First, one that is based on Cartesian quintic splines with optimized parameters. Second, a reinforcement learning-based approach that utilizes the first approach as a reference motion to guide the learning. Both approaches were tested on the developed robot and on further simulated robots to show their generalization. A special focus was set on the locomotion skill, but a standing-up and kick skill are also discussed. Diese Dissertation beschäftigt sich mit dem Lernen von Bewegungsfähigkeiten für humanoide Roboter. Als Grundlage wurde zunächst ein humanoider Roboter mit integriertem Fall Management entwickelt, welcher als Experimentalplatform dient. Dann wurden zwei verschiedene Ansätze für die Erstellung von Bewegungsfähigkeiten untersucht. Zu erst einer der kartesische quintische Splines mit optimierten Parametern nutzt. Danach wurde ein Ansatz basierend auf bestärkendem Lernen untersucht, welcher den ersten Ansatz als Referenzbewegung benutzt. Beide Ansätze wurden sowohl auf der entwickelten Roboterplatform, als auch auf weiteren simulierten Robotern getestet um die Generalisierbarkeit zu zeigen. Ein besonderer Fokus wurde auf die Fähigkeit des Gehens gelegt, aber auch Aufsteh- und Schussfähigkeiten werden diskutiert

Proceedings of the Post-Graduate Conference on Robotics and Development of Cognition, 10-12 September 2012, Lausanne, Switzerland

The aim of the Postgraduate Conference on Robotics and Development of Cognition (RobotDoC-PhD) is to bring together young scientists working on developmental cognitive robotics and its core disciplines. The conference aims to provide both feedback and greater visibility to their research as lively and stimulating discussion can be held amongst participating PhD students and senior researchers. The conference is open to all PhD students and post-doctoral researchers in the field. RobotDoC-PhD conference is an initiative as a part of Marie-Curie Actions ITN RobotDoC and will be organized as a satellite event of the 22nd International Conference on Artificial Neural Networks ICANN 2012

Proceedings of the Post-Graduate Conference on Robotics and Development of Cognition, 10-12 September 2012, Lausanne, Switzerland

The aim of the Postgraduate Conference on Robotics and Development of Cognition (RobotDoC-PhD) is to bring together young scientists working on developmental cognitive robotics and its core disciplines. The conference aims to provide both feedback and greater visibility to their research as lively and stimulating discussion can be held amongst participating PhD students and senior researchers. The conference is open to all PhD students and post-doctoral researchers in the field. RobotDoC-PhD conference is an initiative as a part of Marie-Curie Actions ITN RobotDoC and will be organized as a satellite event of the 22nd International Conference on Artificial Neural Networks ICANN 2012