Evaluating the use of robots to enlarge AAL services

We introduce robots as a tools to enhance Ambient Assisted Living (AAL) services. Robots are a unique opportunity to create new systems to cooperate in reaching better living conditions. Robots offer the possibility of richer interaction with humans, and can perform actions to actively change the environment. The current state-of-art includes skills in various areas, including advanced interaction (natural language, visual attention, object recognition, intention learning), navigation (map learning, obstacle avoidance), manipulation (grasping, use of tools), and cognitive architectures to handle highly unpredictable environments. From our experience in several robotics projects and principally in the RoboCup@Home competition, a new set of evaluation methods is proposed to assess the maturity of the required skills. Such comparison should ideally enable the abstraction from the particular robotic platform and concentrate on the easy comparison of skills. The validity of that low-level skills can be then scaled to more complex tasks, that are composed by several skills. Our conclusion is that effective evaluation methods can be designed with the objective of enabling robots to enlarge AAL services.This research was partly supported by the PATRICIA project (TIN2012-38416-C03-01), MANIPlus project (201350E102), Spanish Ministry of Economy and Competitiveness, and European Found for Regional Development (FEDER).Peer Reviewe

Humanoid Robot Soccer Locomotion and Kick Dynamics: Open Loop Walking, Kicking and Morphing into Special Motions on the Nao Robot

Striker speed and accuracy in the RoboCup (SPL) international robot soccer league is becoming increasingly important as the level of play rises. Competition around the ball is now decided in a matter of seconds. Therefore, eliminating any wasted actions or motions is crucial when attempting to kick the ball. It is common to see a discontinuity between walking and kicking where a robot will return to an initial pose in preparation for the kick action. In this thesis we explore the removal of this behaviour by developing a transition gait that morphs the walk directly into the kick back swing pose. The solution presented here is targeted towards the use of the Aldebaran walk for the Nao robot. The solution we develop involves the design of a central pattern generator to allow for controlled steps with realtime accuracy, and a phase locked loop method to synchronise with the Aldebaran walk so that precise step length control can be activated when required. An open loop trajectory mapping approach is taken to the walk that is stabilized statically through the use of a phase varying joint holding torque technique. We also examine the basic princples of open loop walking, focussing on the commonly overlooked frontal plane motion. The act of kicking itself is explored both analytically and empirically, and solutions are provided that are versatile and powerful. Included as an appendix, the broader matter of striker behaviour (process of goal scoring) is reviewed and we present a velocity control algorithm that is very accurate and efficient in terms of speed of execution

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

Automated Semantic Content Extraction from Images

In this study, an automatic semantic segmentation and object recognition methodology is implemented which bridges the semantic gap between low level features of image content and high level conceptual meaning. Semantically understanding an image is essential in modeling autonomous robots, targeting customers in marketing or reverse engineering of building information modeling in the construction industry. To achieve an understanding of a room from a single image we proposed a new object recognition framework which has four major components: segmentation, scene detection, conceptual cueing and object recognition. The new segmentation methodology developed in this research extends Felzenswalb\u27s cost function to include new surface index and depth features as well as color, texture and normal features to overcome issues of occlusion and shadowing commonly found in images. Adding depth allows capturing new features for object recognition stage to achieve high accuracy compared to the current state of the art. The goal was to develop an approach to capture and label perceptually important regions which often reflect global representation and understanding of the image. We developed a system by using contextual and common sense information for improving object recognition and scene detection, and fused the information from scene and objects to reduce the level of uncertainty. This study in addition to improving segmentation, scene detection and object recognition, can be used in applications that require physical parsing of the image into objects, surfaces and their relations. The applications include robotics, social networking, intelligence and anti-terrorism efforts, criminal investigations and security, marketing, and building information modeling in the construction industry. In this dissertation a structural framework (ontology) is developed that generates text descriptions based on understanding of objects, structures and the attributes of an image

Aprendizagem automática de comportamentos para futebol robótico

Mestrado em Engenharia de Computadores e TelemáticaNo desenvolvimento de um agente inteligente e necess ario criar um conjunto de comportamentos, mais ou menos complexos, para que o agente possa escolher o que achar mais adequado para utilizar a cada instante. Comportamentos simples podem ser facilmente programados \ a m~ao", mas, a medida que se tentam criar comportamentos mais complexos esta tarefa pode tornar-se invi avel. Isto pode acontecer, por exemplo, em casos onde o espa co de estados, o espa co de a c~oes e/ou o tempo tomam valores cont nuos. E esse o caso no futebol rob otico, onde os rob^os se movem num espa co cont nuo, com velocidades e em tempo cont nuos. A aprendizagem por refor co permite que seja o agente a aprender um comportamento atrav es da sua experi^encia ao interagir com o mundo. Esta t ecnica baseia-se num mecanismo que ocorre na natureza, uma vez que imita a forma como os animais aprendem, mais concretamente, observando o estado do mundo, tomando uma a c~ao e observando as consequ^encias dessa a c~ao. A longo prazo, e com base nas consequ^encias das a c~oes tomadas, o animal aprende se, nessas circunst^ancias, a sequ^encia de a c~oes que o levaram a esse ponto e boa e pode ser repetida ou n~ao. Para que o agente aprenda da mesma forma, e preciso que consiga percecionar o valor que as suas a c~oes t^em a longo prazo. Para isso, e-lhe dada uma recompensa ou um castigo quando faz uma a c~ao desejada ou indesejada, respetivamente. Comportamentos aprendidos podem ser usados em situa c~oes em que e invi avel escrev^e-los a m~ao, ou para criar comportamentos com melhor desempenho uma vez que o agente consegue derivar fun c~oes complexas que descrevam melhor a solu c~ao do problema. No contexto desta tese foram desenvolvidos 3 comportamentos no contexto da equipa de futebol rob otico CAMBADA da Univeridade de Aveiro. O primeiro comportamento, o mais simples, consistiu em fazer o rob^o rodar sobre si pr oprio at e estar virado para uma dada orienta c~ao absoluta. O segundo permitia que o rob^o, com a bola na sua posse, a driblasse numa dire c~ao desejada. Por m, o terceiro comportamento permitiu que o rob^o aprendesse a ajustar a sua posi c~ao para receber uma bola que pode vir com mais ou menos velocidade e descentrada em rela c~ao ao receptor. Os resultados das compara c~oes feitas com os comportamentos desenvolvidos a m~ao que j a existiam na CAMBADA, mostram que comportamentos aprendidos conseguem ser mais e cientes e obter melhores resultados do que os explicitamente programados.While developing an intelligent agent, one needs to create a set of behaviors, more or less complex, to allow the agent to choose the one it believes to be appropriate at each instant. Simple behaviors can easily be developed by hand, but, as we try to create more complex ones, this becomes impracticable. This complexity may arise, for example, when the state space, the action space and/or the time take continuous values. This is the case of robotic soccer where the robots move in a continuous space, at continuous velocities and in continuous time. Reinforcement learning enables the agent to learn behaviors by itself by experiencing and interacting with the world. This technique is based on a mechanism which happens in nature, since it mimics the way animals learn, more precisely, observing the world state, taking an action and then observe the consequences of that action. In the long run, and based on the consequences of the actions taken, the animal learned if, in those circumstances, the sequence of actions which led it to that state is good and may be repeated or not. To make the agent learn in this way, it must understand the value of its actions in the long run. In order to do that, it is given a reward or a punishment for doing a desired or undesired action, respectively. Learned behaviors can be used in cases where they are too complex to be written by hand, or to create behaviors that can perform better than the hand-coded ones, since the agent can derive complex functions that better describe a solution for the given problem. During this thesis, 3 behaviors were developed in the context of the robotic soccer CAMBADA team from University of Aveiro. The rst behavior, the most simple, made the robot rotate about itself until it had turned to a given absolute orientation. The second one, allowed a robot that possessed the ball to dribble it in a desired direction. Lastly, the third behavior allowed the robot to learn to adjust its position to receive a ball. The ball can come at a high or low speed and may not be centered in relation to the receiver. The results of comparing the learned behaviors to the already existing handcoded ones showed that the learned behaviors were more e cient and obtained better results

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