Multi-feature Bottom-up Processing and Top-down Selection for an Object-based Visual Attention Model

Artificial vision systems can not process all the information that they receive from the world in real time because it is highly expensive and inefficient in terms of computational cost. However, inspired by biological perception systems, it is possible to develop an artificial attention model able to select only the relevant part of the scene, as human vision does. This paper presents an attention model which draws attention over perceptual units of visual information, called proto-objects, and which uses a linear combination of multiple low-level features (such as colour, symmetry or shape) in order to calculate the saliency of each of them. But not only bottom-up processing is addressed, the proposed model also deals with the top-down component of attention. It is shown how a high-level task can modulate the global saliency computation, modifying the weights involved in the basic features linear combination.Ministerio de Economía y Competitividad (MINECO), proyectos: TIN2008-06196 y TIN2012-38079-C03-03. Campus de Excelencia Internacional Andalucía Tech

Special Issue on Advances on Physical Agents 2016

The Workshop on Physical Agents is a forum for information and experience exchange in different areas regarding the concept of embodied agents, especially applied to the control and coordination of autonomous systems: robots, mobile robots, domotics, agents, industrial applications or complex systems. This special issue brings together a selection of revised and extended papers that were first presented at the XVII Workshop on Physical Agents (WAF’2016), which was held on June 16-17, 2016 at the School of Telecommunication Engineering and Information Technology of the University of Málaga (Spain)

Special Issue “Cognitive Robotics”

Within the realm of new robotics, researchers have placed a great amount of effort into learning, understanding, and representing knowledge for task execution by robots. The goal is to develop robots that can help humans with daily tasks. Cognitive robots need to explore and understand their environment, choose a safe and human-aware course of action, and learn—not only from experience, but also through interaction. This Special Issue collects nine research papers in various fields related to Cognitive robotics. The relevance of the knowledge representation and its use by decision makers is present in the proposal by Martín et al. [1]. Specifically, the necessity of integrating behaviors and symbolic knowledge was solved by adding a graph-based working memory to a cognitive robotics architecture. The proposed framework has been successfully tested in robotics competitions such as the RoboCup and the European Robotics League. The aim of combining deliberative and reactive behaviors in a flexible way is also present in the work by González-Santamarta et al. [2]. In the MERLIN cognitive architecture, the process of integrating deliberative and behavioral-based mechanisms in robotics is normalized. The solution is empirically tested using a variation of the challenge defined in the SciRoc @ home competition. The relevance that cognitive robots can provide for improving task effectiveness and productivity in the industrial domain is highlighted in the work by Chacón et al. [3]. 8. (...)This work has been partially funded by the RTI2018-099522-B-C41 project, funded by the Spanish Ministerio de Ciencia, Innovación y Universidades and FEDER funds

CLARC project

El proyecto CLARC propone el diseño, desarrollo y validación de una plataforma robótica que sirva de herramienta práctica para médicos de geriatría en la realización de tests de situación a personas mayores. En el marco de la reunión de evaluación del proyecto europeo en que se integra este desarrollo (ECHORD++), esta presentación describe el estado actual del proyecto CLARC.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Apuntes de Planificación, gestión y desarrollo de proyectos

Apuntes de la asignatura Planificación, gestión y desarrollo de proyectos del Máster en Sistemas Electrónicos para Entornos Inteligente

Towards the development of cognitive robotics

Esta charla describe una propuesta de arquitectura cognitiva para el desarrollo de robots que sean capaces de anticipar el resultado de sus acciones y, por tanto, desarrollar su actividad en un entorno compartido con personas. La propuesta se justifica teoricamente en postulados inspirados en como funciona nuestro cerebro.Proyecto TIN2012-38079-C03-03 del MINECO y fondos FEDE

Integration of the Alexa assistant as a voice interface for robotics platforms

Virtual assistants such as Cortana or Google Assistant are becoming familiar devices in everyday environments, where they are used to control real devices through natural language. This paper extends this application scenario, and it describes the use of the Alexa assistant from Amazon through an Echo dot device to drive the behaviour of a robotic platform. The paper focuses on the description of the technologies employed to set such ecosystem. Significantly, the proposed architecture is based, from the remote server to the on-board controllers, in LowEnergy (LE) hardware and a scalable software platform. This approach will ease programmers integrating different platforms, e.g. mobile-based applications to control robots or home-made devices.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

From perception to action and vice versa: a new architecture showing how perception and action can modulate each other simultaneously

Presentado en: 6th European Conference on Mobile Robots (ECMR) Sep 25-27, 2013 Barcelona, SpainArtificial vision systems can not process all the information that they receive from the world in real time because it is highly expensive and inefficient in terms of computational cost. However, inspired by biological perception systems, it is possible to develop an artificial attention model able to select only the relevant part of the scene, as human vision does. From the Automated Planning point of view, a relevant area can be seen as an area where the objects involved in the execution of a plan are located. Thus, the planning system should guide the attention model to track relevant objects. But, at the same time, the perceived objects may constrain or provide new information that could suggest the modification of a current plan. Therefore, a plan that is being executed should be adapted or recomputed taking into account actual information perceived from the world. In this work, we introduce an architecture that creates a symbiosis between the planning and the attention modules of a robotic system, linking visual features with high level behaviours. The architecture is based on the interaction of an oversubscription planner, that produces plans constrained by the information perceived from the vision system, and an object-based attention system, able to focus on the relevant objects of the plan being executed.Spanish MINECO projects TIN2008-06196, TIN2012-38079-C03-03 and TIN2012-38079-C03-02. Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tec

RoboARCH: An autonomous robot for analysis and documentation of historical architectures

The Mediterranean basin has an impressive amount of millenarian urban structures which have been modelled along centuries. Unfortunately, they are sometimes damaged due to both the passage of time as well as bad preservation criteria. In order to avoid these situations or reduce their effects, new preservation criteria have arisen in the last decades. These criteria aim to revitalize the historical value of these architectural remains from a cultural and economic perspective. In this line of research, the “Archaeology of Architecture” applies the theoretical principles of the archaeology to study buildings and streets, offering new methodologies of analysis. An important part of these methodologies incorporates new technologies, such as 3D scanners, robotic total stations, or virtual and augmented reality, to the data acquisition and processing tasks. The application of these technologies in the area of Historical Heritage results in a breakthrough in the graphic documentation of monuments and archaeological remains, which allows the development of new preservation strategies. Among all these new technologies, this abstract proposes the use of an autonomous robot to help identifying elements inside a building. The robot navigates through the environment, collects data and compares them against well-known historical and architectural archetypes, to find a set of candidates for each perceived pattern. The advantages of the proposed system when compared against current state-of-the-art techniques are the following: (i) the robot explores the environment autonomously using SLAM (Simultaneous Localization And Mapping) algorithms and acquires colour and depth information; (ii) no special markers, such as the targets or spheres usually employed by robotic total stations, are required; (iii) the system uses advanced image processing methods to automatically provide a first characterization of perceived borders, that will help in different identification processes, from single elements to more complex structures; (iv) obtained data are compared against historical and architectural archetypes included in a data base; (v) evaluation of the object position inside the stratigraphic sequence of the wall.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Topology-preserving perceptual segmentation using the Combinatorial Pyramid

Scene understanding and other high-level visual tasks usually rely on segmenting the captured images for dealing with a more efficient mid-level representation. Although this segmentation stage will consider topological constraints for the set of obtained regions (e.g., their internal connectivity), it is typical that the importance of preserving the topological relationships among regions will be not taken into account. Contrary to other similar approaches, this paper presents a bottom-up approach for perceptual segmentation of natural images which preserves the topology of the image. The segmentation algorithm consists of two consecutive stages: firstly, the input image is partitioned into a set of blobs of uniform colour (pre-segmentation stage) and then, using a more complex distance which integrates edge and region descriptors, these blobs are hierarchically merged (perceptual grouping). Both stages are addressed using the Combinatorial Pyramid, a hierarchical structure which can correctly encode relationships among image regions at upper levels. The performance of the proposed approach has been initially evaluated with respect to groundtruth segmentation data using the Berkeley Segmentation Dataset and Benchmark. Although additional descriptors must be added to deal with small regions and textured surfaces, experimental results reveal that the proposed perceptual grouping provides satisfactory scores