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 “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

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

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

Towards Active Image Segmentation: the Foveal Bounded Irregular Pyramid

Presentado en: 2nd workshop on Recognition and Action for Scene Understanding York, Inglaterra August 30, 2013It is well established that the units of attention on human vision are not merely spatial but closely related to perceptual objects. This implies a strong relationship between segmentation and attention processes. This interaction is bi-directional: if the segmentation process constraints attention, the way an image is segmented may depend on the specific question asked to an observer, i.e. what she 'attend' in this sense. When the focus of attention is deployed from one visual unit to another, the rest of the scene is perceived but at a lower resolution that the focused object. The result is a multi-resolution visual perception in which the fovea, a dimple on the central retina, provides the highest resolution vision. While much work has recently been focused on computational models for object-based attention, the design and development of multi-resolution structures that can segment the input image according to the focused perceptual unit is largely unexplored. This paper proposes a novel structure for multi-resolution image segmentation that extends the encoding provided by the Bounded Irregular Pyramid. Bottom-up attention is enclosed in the same structure, allowing to set the fovea over the most salient image region. Preliminary results obtained from the segmentation of natural images show that the performance of the approach is good in terms of speed and accuracy.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

On Managing Knowledge for MAPE-K Loops in Self-Adaptive Robotics Using a Graph-Based Runtime Model

Service robotics involves the design of robots that work in a dynamic and very open environment, usually shared with people. In this scenario, it is very difficult for decision-making processes to be completely closed at design time, and it is necessary to define a certain variability that will be closed at runtime. MAPE-K (Monitor–Analyze–Plan–Execute over a shared Knowledge) loops are a very popular scheme to address this real-time self-adaptation. As stated in their own definition, they include monitoring, analysis, planning, and execution modules, which interact through a knowledge model. As the problems to be solved by the robot can be very complex, it may be necessary for several MAPE loops to coexist simultaneously in the robotic software architecture endowed in the robot. The loops will then need to be coordinated, for which they can use the knowledge model, a representation that will include information about the environment and the robot, but also about the actions being executed. This paper describes the use of a graph-based representation, the Deep State Representation (DSR), as the knowledge component of the MAPE-K scheme applied in robotics. The DSR manages perceptions and actions, and allows for inter- and intra-coordination of MAPE-K loops. The graph is updated at runtime, representing symbolic and geometric information. The scheme has been successfully applied in a retail intralogistics scenario, where a pallet truck robot has to manage roll containers for satisfying requests from human pickers working in the warehousePartial funding for open access charge: Universidad de Málaga. This work has been partially developed within SA3IR (an experiment funded by EU H2020 ESMERA Project under Grant Agreement 780265), the project RTI2018-099522-B-C4X, funded by the Gobierno de España and FEDER funds, and the B1-2021_26 project, funded by the University of Málaga

Testing a fully autonomous robotic salesman in real scenarios

Over the past decades, the number of robots deployed in museums, trade shows and exhibitions have grown steadily. This new application domain has become a key research topic in the robotics community. Therefore, new robots are designed to interact with people in these domains, using natural and intuitive channels. Visual perception and speech processing have to be considered for these robots, as they should be able to detect people in their environment, recognize their degree of accessibility and engage them in social conversations. They also need to safely navigate around dynamic, uncontrolled environments. They must be equipped with planning and learning components, that allow them to adapt to different scenarios. Finally, they must attract the attention of the people, be kind and safe to interact with. In this paper, we describe our experience with Gualzru, a salesman robot endowed with the cognitive architecture RoboCog. This architecture synchronizes all previous processes in a social robot, using a common inner representation as the core of the system. The robot has been tested in crowded, public daily life environments, where it interacted with people that had never seen it before nor had a clue about its functionality. Experimental results presented in this paper demonstrate the capabilities of the robot and its limitations in these real scenarios, and define future improvement actions.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

The cognitive architecture of a robotic salesman

This paper describes a robotics cognitive architecture for social robots named CORTEX. This architecture integrates di fferent levels of abstraction (from basic geometry to high-level predicates) into a unique Deep Space Representation (DSR) that diff erent agents interface. These agents update the contents of the DSR with new data from the outer world, and execute, plan and design behaviours. The design of CORTEX as an unified deep representation allows to fit both the subsymbolic processing and exibility requirements of robot control. In this paper a first implementation of CORTEX has been integrated into Gualzru, a robotic salesman, and tested in real scenarios. Results show that this cognitive architecture allows this robot to adequately execute its use case, and that it has a promising adaptability to achieve new tasks and be used in new scenarios.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech