An Experiment in Morphological Development for Learning ANN Based Controllers

Morphological development is part of the way any human or animal learns. The learning processes starts with the morphology at birth and progresses through changing morphologies until adulthood is reached. Biologically, this seems to facilitate learning and make it more robust. However, when this approach is transferred to robotic systems, the results found in the literature are inconsistent: morphological development does not provide a learning advantage in every case. In fact, it can lead to poorer results than when learning with a fixed morphology. In this paper we analyze some of the issues involved by means of a simple, but very informative experiment in quadruped walking. From the results obtained an initial series of insights on when and under what conditions to apply morphological development for learning are presented.Comment: 10 pages, 4 figures. arXiv admin note: text overlap with arXiv:2003.0581

Guiding the Exploration of the Solution Space in Walking Robots Through Growth-Based Morphological Development

In human beings, the joint development of the body and cognitive system has been shown to facilitate the acquisition of new skills and abilities. In the literature, these natural principles have been applied to robotics with mixed results and different authors have suggested several hypotheses to explain them. One of the most popular hypotheses states that morphological development improves learning by increasing exploration of the solution space, avoiding stagnation in local optima. In this article, we are going to study the influence of growth-based morphological development and its nuances as a tool to improve the exploration of the solution space. We will perform a series of experiments over two different robot morphologies which learn to walk. Furthermore, we will compare these results to another optimization strategy that has been shown to be useful to favor exploration in learning algorithms: the application of noise during learning. Finally, to check if the increased exploration hypothesis holds, we visualize the genotypic space during learning considering the different optimization strategies by using the Search Trajectory Network representation. The results indicate that noise and growth increase exploration, but only growth guides the search towards good solutions

Robobo: la siguiente generación de robot educativo

[Resumen] Las carreras universitarias de informática e ingeniería han estado utilizando robots móviles en diferentes asignaturas desde hace mucho tiempo. Hasta el momento, debido principalmente a limitaciones económicas, estos robots educativos han sido bastante simples en términos tecnológicos. Esto no era un gran problema porque la robótica no se consideraba un mercado real en la industria, por lo que los robots se usaban en las clases como prototipos, sin una expectativa de aplicación a la realidad. Pero como todos sabemos, la situación actual, y el futuro cercano, hacen que la robótica sea un mercado clave para los ingenieros y los informáticos que se forman en las universidades. Como consecuencia, los robots utilizados en las aulas universitarias deben ser actualizados para adecuarlos a la realidad tecnológica que se maneja en el mundo industrial. Este artículo presenta Robobo, un robot móvil educativo de bajo costo desarrollado en la Universidade da Coruña. Robobo combina una base con ruedas simple con un teléfono inteligente, que proporciona la última tecnología al robot. Con Robobo, los estudiantes pueden desarrollar sus propios proyectos usando cámaras, micrófonos o pantallas de alta resolución, acercando la enseñanza universitaria al mercado real que encontrarán cuando finalicen sus estudios.[Abstract] Computer science and engineering majors have been using mobile robots in different subjects for a long time. So far, due primarily to economic constraints, these educational robots have been quite simple in technological terms. This was not a big problem because robotics was not considered a real market in the industry, so robots were used in classes as prototypes, without an expectation of application to reality. But as we all know, the current situation, and the near future, make robotics a key market for engineers and IT graduates in universities. As a consequence, the robots used in the university classrooms must be updated to adapt them to the technological reality that is handled in the industrial world. This article presents Robobo, a low cost educational mobile robot developed at the University of Coruña. Robobo combines a simple wheeled base with a smartphone, which provides the latest technology to the robot. With Robobo, students can develop their own projects using cameras, microphones or highresolution displays, bringing university education closer to the real market they will find when they finish their studies

Engineering Morphological Development in a Robotic Bipedal Walking Problem: An Empirical Study

Financiado para publicación en acceso aberto: Universidade da Coruña/CISUG[Abstract]: In living beings, the natural development of the body has been shown to facilitate learning. The application of these natural developmental principles in robotics have been considered in different robotic morphologies and scenarios, leading to mixed results. Development was found to be beneficial for learning in some instances, but also irrelevant or detrimental in others. This mix of results and scenarios has allowed researchers to extract some notions about the conditions that must be fulfilled or set to apply morphological development successfully. Notions that we have organized to set a series of design conditions to successfully apply morphological development. Thus, in this article, we are going to focus on the study of one of them that has been frequently addressed by researchers in their studies in very general terms. It can be described as the need to achieve a suitable synergy among the different components involved in the development and learning process: morphological development strategy, controller, task, and learning algorithm. In particular, we have concentrated on empirically determining the influence of five developmental strategies, implemented in different ways, applied at different speeds and deployed in different orders and combinations, over the problem of a NAO robot controlled by an artificial neural network obtained through a neuroevolutionary algorithm learning a bipedal walking task. The results obtained permit providing a more detailed description of what a suitable synergy implies and how it can be utilized to design more successful morphological developmental processes to improve robot learning.Xunta de Galicia ; EDC431C-2021/39Research supported by the European Commission Horizon program PILLAR-Robots project, grant 101070381, the Xunta de Galicia and the European Regional Development Funds under grant EDC431C-2021/39 and the Spanish Science and Education Ministry through grant PID2021-126220OB-100. We wish to acknowledge the support received from the Centro de Investigación de Galicia ‘‘CITIC”, funded by Xunta de Galicia and the European Union (European Regional Development Fund-Galicia 2014-2020 Program), by grant ED431G 2019/01 and the Centro de Supercomputación de Galicia (CESGA)Xunta de Galicia; ED431G 2019/0

An Approach for the Customized High-Dimensional Segmentation of Remote Sensing Hyperspectral Images

Abstract: This paper addresses three problems in the field of hyperspectral image segmentation: the fact that the way an image must be segmented is related to what the user requires and the application; the lack and cost of appropriately labeled reference images; and, finally, the information loss problem that arises in many algorithms when high dimensional images are projected onto lower dimensional spaces before starting the segmentation process. To address these issues, the Multi-Gradient based Cellular Automaton (MGCA) structure is proposed to segment multidimensional images without projecting them to lower dimensional spaces. The MGCA structure is coupled with an evolutionary algorithm (ECAS-II) in order to produce the transition rule sets required by MGCA segmenters. These sets are customized to specific segmentation needs as a function of a set of low dimensional training images in which the user expresses his segmentation requirements. Constructing high dimensional image segmenters from low dimensional training sets alleviates the problem of lack of labeled training images. These can be generated online based on a parametrization of the desired segmentation extracted from a set of examples. The strategy has been tested in experiments carried out using synthetic and real hyperspectral images, and it has been compared to state-of-the-art segmentation approaches over benchmark images in the area of remote sensing hyperspectral imaging.Ministerio de Economía y competitividad; TIN2015-63646-C5-1-RMinisterio de Economía y competitividad; RTI2018-101114-B-I00Xunta de Galicia: ED431C 2017/1