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Generation of Human-aware Navigation Maps using Graph Neural Networks

Daniel Rodriguez-Criado, Pilar Bachiller and Luis J. Manso
Conference Papers arXiv preprint: 2011.05180

BibTeX

@inproceedings{rodriguezcriado2020generation,
    author="Daniel Rodriguez-Criado and Pilar Bachiller and Luis J. Manso",
    title="Generation of Human-aware Navigation Maps using Graph Neural Networks",
    booktitle="arXiv prepring: 2011.05180",
    year="2020",
    pages="6",
}

Abstract

Minimising the discomfort caused by robots when navigating in social situations is crucial for them to be accepted. The paper presents a machine learning-based framework that bootstraps existing one-dimensional datasets to generate a cost map dataset and a model combining Graph Neural Network and Convolutional Neural Network layers to produce cost maps for human-aware navigation in real-time. The proposed framework is evaluated against the original one-dimensional dataset and in simulated navigation tasks. The results outperform similar state-of-the-art-methods considering the accuracy on the dataset and the navigation metrics used. The applications of the proposed framework are not limited to human-aware navigation, it could be applied to other fields where map generation is needed.

Graph Neural Networks for Human-aware Social Navigation

Luis J. Manso, Ronit R. Jorvekar, Diego Faria, Pablo Bustos and Pilar Bachiller
Book Chapters Advances in Physical Agents II, pp. 167-179, ISBN 978-3-030-62578-8, Springer 2020.

BibTeX

@inbook{manso2020graphneural,
    author="Luis J. Manso and Ronit R. Jorvekar and Diego Faria and Pablo Bustos and Pilar Bachiller",
    title="Graph Neural Networks for Human-aware Social Navigation",
    pages="167-179",
    publisher="Springer",
    year="2020",
    type="bookchapter",
}

Abstract

Autonomous navigation is a key skill for assistive and service robots. To be successful, robots have to navigate avoiding going through the personal spaces of the people surrounding them. Complying with social rules such as not getting in the middle of human-to-human and human-to-object interactions is also important. This paper suggests using Graph Neural Networks to model how inconvenient the presence of a robot would be in a particular scenario according to learned human conventions so that it can be used by path planning algorithms. To do so, we propose two ways of modelling social interactions using graphs and benchmark them with different Graph Neural Networks using the SocNav1 dataset. We achieve close-to-human performance in the dataset and argue that, in addition to promising results, the main advantage of the approach is its scalability in terms of the number of social factors that can be considered and easily embedded in code, in comparison with model-based approaches. The code used to train and test the resulting graph neural network is available in a public repository.

A Toolkit to Generate Social Navigation Datasets

Rishabh Baghel, Aditya Kapoor, Pilar Bachiller, Ronit R. Jorvekar, Daniel Rodriguez-Criado and Luis J. Manso
Book Chapters Advances in Physical Agents II, pp 180-193, ISBN 978-3-030-62578-8, Springer 2020.

BibTeX

@inbook{baghel2020atoolkit,
    author="Rishabh Baghel and Aditya Kapoor and Pilar Bachiller and Ronit R. Jorvekar and Daniel Rodriguez-Criado and Luis J. Manso",
    title="A Toolkit to Generate Social Navigation Datasets",
    pages="180-193",
    publisher="Springer",
    year="2020",
    type="bookchapter",
}

Abstract

Social navigation datasets are necessary to assess social navigation algorithms and train machine learning algorithms. Most of the currently available datasets target pedestrians' movements as a pattern to be replicated by robots. It can be argued that one of the main reasons for this to happen is that compiling datasets where real robots are manually controlled, as they would be expected to behave when moving, is a very resource-intensive task. Another aspect that is often missing in datasets is symbolic information that could be relevant, such as human activities, relationships or interactions. Unfortunately, the available datasets targeting robots and supporting symbolic information are restricted to static scenes. This paper argues that simulation can be used to gather social navigation data in an effective and cost-efficient way and presents a toolkit for this purpose. A use case studying the application of graph neural networks to create learned control policies using supervised learning is presented as an example of how it can be used.

Multi-camera Torso Pose Estimation using Graph Neural Networks

Daniel Rodriguez-Criado, Pilar Bachiller, Pablo Bustos, George Vogiatzis and Luis J. Manso
Conference Papers IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)

Multi-camera Torso Pose Estimation using Graph Neural Networks

Daniel Rodriguez-Criado, Pilar Bachiller, Pablo Bustos, George Vogiatzis and Luis J. Manso
Conference Papers IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)

Multi-camera Torso Pose Estimation using Graph Neural Networks

Daniel Rodriguez-Criado, Pilar Bachiller, Pablo Bustos, George Vogiatzis and Luis J. Manso
Conference Papers IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)

BibTeX

@inproceedings{rodriguezcriado2020multicamera,
    author="Daniel Rodriguez-Criado and Pilar Bachiller and Pablo Bustos and George Vogiatzis and Luis J. Manso",
    title="Multi-camera Torso Pose Estimation using Graph Neural Networks",
    booktitle="The 29th IEEE International Conference on Robot and Human Interactive Communication",
    year="2020",
    pages="6",
}

Abstract

Estimating the location and orientation of humans is an essential skill for service and assistive robots. To achieve a reliable estimation in a wide area such as an apartment, multiple RGBD cameras are frequently used. Firstly, these setups are relatively expensive. Secondly, they seldom perform an effective data fusion using the multiple camera sources at an early stage of the processing pipeline. Occlusions and partial views make this second point very relevant in these scenarios. The proposal presented in this paper makes use of graph neural networks to merge the information acquired from multiple camera sources, achieving a mean absolute error below 125 mm for the location and 10 degrees for the orientation using low-resolution RGB images. The experiments, conducted in an apartment with three cameras, benchmarked two different graph neural network implementations and a third architecture based on fully connected layers. The software used has been released as open-source in a public repository.

LearnBlock: A Robot-Agnostic Educational Programming Tool

Pilar Bachiller-Burgos, Ivan Barbecho, Luis V. Calderita, Pablo Bustos and Luis J. Manso
Journal Papers Access, IEEE, ISSN 2169-3536, Vol 8, no. 1, pp. 30012-30026. February 2020. DOI: 10.1109/ACCESS.2020.2972410. 2020.

BibTeX

@article{bachiller2020learnblock,
    author="Pilar Bachiller-Burgos and Ivan Barbecho and Luis V. Calderita and Pablo Bustos and Luis J. Manso",
    title="LearnBlock: A Robot-Agnostic Educational Programming Tool",
    journal="Access",
    year="2020",
    volume="8",
    number="1",
    pages="30012-30026",
    doi="https://doi.org/10.1109/ACCESS.2020.2972410",
}

Abstract

Education is evolving to prepare students for the current sociotechnical changes. An increasing effort to introduce programming and other STEM-related subjects into the core curriculum of primary and secondary education is taking place around the world. The use of robots stands out among STEM initiatives, since robots are proving to be an engaging tool for learning programming and other STEM-related contents. Block-based programming is the option chosen for most educational robotic platforms. However, many robotics kits include their own software tools, as well as their own set of programming blocks. LearnBlock, a new educational programming tool, is proposed here. Its major novelty is its loosely coupled software architecture which makes it, to the best of our knowledge, the first robot-agnostic educational tool. Robot-agnosticism is provided not only in block code, but also in generated code, unifying the translation from blocks to the final programming language. The set of blocks can be easily extended implementing additional Python functions, without modifying the core code of the tool. Moreover, LearnBlock provides an integrated educational programming environment that facilitates a progressive transition from a visual to a general-purpose programming language. To evaluate LearnBlock and demonstrate that it is platform-agnostic, several tests were conducted. Each of them consists of a program implementing a robot behaviour. The block code of each test can run on several educational robots without changes.

SocNav1: A Dataset to Benchmark and Learn Social Navigation Conventions

Luis J. Manso, Pedro Núñez, Luis V. Calderita, Diego R. Faria and Pilar Bachiller
Journal Papers Data, MDPI, ISSN 2306-5729, Vol 5, no. 1, pp. 10. January 2020. DOI: 10.3390/data5010007. 2020.

BibTeX

@article{manso2020socnav,
    author="Luis J. Manso and Pedro Núñez and Luis V. Calderita and Diego R. Faria and Pilar Bachiller",
    title="SocNav1: A Dataset to Benchmark and Learn Social Navigation Conventions",
    journal="Data",
    year="2020",
    volume="5",
    number="1",
    pages="10",
    doi="https://doi.org/10.3390/data5010007",
}

Abstract

Datasets are essential to the development and evaluation of machine learning and artificial intelligence algorithms. As new tasks are addressed, new datasets are required. Training algorithms for human-aware navigation is an example of this need. Different factors make designing and gathering data for human-aware navigation datasets challenging. Firstly, the problem itself is subjective, different dataset contributors will very frequently disagree to some extent on their labels. Secondly, the number of variables to consider is undetermined culture-dependent. This paper presents SocNav1, a dataset for social navigation conventions. SocNav1 aims at evaluating the robots’ ability to assess the level of discomfort that their presence might generate among humans. The 9280 samples in SocNav1 seem to be enough for machine learning purposes given the relatively small size of the data structures describing the scenarios. Furthermore, SocNav1 is particularly well-suited to be used to benchmark non-Euclidean machine learning algorithms such as graph neural networks. This paper describes the proposed dataset and the method employed to gather the data. To provide a further understanding of the nature of the dataset, an analysis and validation of the collected data are also presented.

Socially-Accepted Path Planning for Robot Navigation Based on Social Interaction Spaces

Araceli Vega, Ramón Cintas, Luis J. Manso, Pablo Bustos and Pedro Núñez
Conference Papers Proceedings of Iberian Robotics conference 2020, pp. 644-655, Springer. 2019.

BibTeX

@inproceedings{vega2019sociallyaccepted,
    author="Araceli Vega and Ramón Cintas and Luis J. Manso and Pablo Bustos and Pedro Núñez",
    title="Socially-Accepted Path Planning for Robot Navigation Based on Social Interaction Spaces",
    booktitle="Iberian Robotics conference",
    year="2019",
    pages="644-655",
}

Abstract

Path planning is one of the most widely studied problems in robot navigation. It deals with estimating an optimal set of waypoints from an initial to a target coordinate. New generations of assistive robots should be able to compute these paths considering not only obstacles but also social conventions. This ability is commonly referred to as social navigation. This paper describes a new socially-acceptable path-planning framework where robots avoid entering areas corresponding to the personal spaces of people, but most importantly, areas related to human-human and human-object interaction. To estimate the social cost of invading personal spaces we use the concept of proxemics. To model the social cost of invading areas where interaction is happening we include the concept of object interaction space. The framework uses Dijkstra’s algorithm on a uniform graph of free space where edges are weighed according to the social traversal cost of their outbound node. Experimental results demonstrate the validity of the proposal to plan socially-accepted paths.

Perceptions or Actions? Grounding How Agents Interact Within a Software Architecture for Cognitive Robotics

Rebeca Marfil, Adrian Romero-Garces, Juan P. Bandera, Luis J. Manso, Luis V. Calderita, Pablo Bustos, Antonio Bandera, Javier Garcia-Polo, Fernando Fernandez and Dimitry Voilmy
Journal Papers Cognitive Computation, Springer, Vol 2019, pp. 27. November 2019. DOI: 10.1007/s12559-019-09685-5. 2019.

BibTeX

@article{marfil2019perceptions,
    author="Rebeca Marfil and Adrian Romero-Garces and Juan P. Bandera and Luis J. Manso and Luis V. Calderita and Pablo Bustos and Antonio Bandera and Javier Garcia-Polo and Fernando Fernandez and Dimitry Voilmy",
    title="Perceptions or Actions? Grounding How Agents Interact Within a Software Architecture for Cognitive Robotics",
    journal="Cognitive Computation",
    year="2019",
    volume="2019",
    pages="27",
    doi="https://doi.org/10.1007/s12559-019-09685-5",
}

Abstract

One of the aims of cognitive robotics is to endow robots with the ability to plan solutions for complex goals and then to enact those plans. Additionally, robots should react properly upon encountering unexpected changes in their environment that are not part of their planned course of actions. This requires a close coupling between deliberative and reactive control flows. From the perspective of robotics, this coupling generally entails a tightly integrated perceptuomotor system, which is then loosely connected to some specific form of deliberative system such as a planner. From the high-level perspective of automated planning, the emphasis is on a highly functional system that, taken to its extreme, calls perceptual and motor modules as services when required. This paper proposes to join the perceptual and acting perspectives via a unique representation where the responses of all software modules in the architecture are generalized using the same set of tokens. The proposed representation integrates symbolic and metric information. The proposed approach has been successfully tested in CLARC, a robot that performs Comprehensive Geriatric Assessments of elderly patients. The robot was favourably appraised in a survey conducted to assess its behaviour. For instance, using a 5-point Likert scale from 1 (strongly disagree) to 5 (strongly agree), patients reported an average of 4.86 when asked if they felt confident during the interaction with the robot. This paper proposes a mechanism for bringing the perceptual and acting perspectives closer within a distributed robotics architecture. The idea is built on top of the blackboard model and scene graphs. The modules in our proposal communicate using a short-term memory, writing the perceptual information they need to share with other agents and accessing the information they need for determining the next goals to address.

Socially-Accepted Path Planning for Robot Navigation Based on Social Interaction Spaces

Araceli Vega, Ramón Cintas, Luis J. Manso, Pablo Bustos and Pedro Núñez
Conference Papers Proceedings of Iberian Robotics conference 2020, pp. 644-655, Springer. 2019.

BibTeX

@inproceedings{vega2019sociallyaccepted,
    author="Araceli Vega and Ramón Cintas and Luis J. Manso and Pablo Bustos and Pedro Núñez",
    title="Socially-Accepted Path Planning for Robot Navigation Based on Social Interaction Spaces",
    booktitle="Iberian Robotics conference",
    year="2019",
    pages="644-655",
}

Abstract

Path planning is one of the most widely studied problems in robot navigation. It deals with estimating an optimal set of waypoints from an initial to a target coordinate. New generations of assistive robots should be able to compute these paths considering not only obstacles but also social conventions. This ability is commonly referred to as social navigation. This paper describes a new socially-acceptable path-planning framework where robots avoid entering areas corresponding to the personal spaces of people, but most importantly, areas related to human-human and human-object interaction. To estimate the social cost of invading personal spaces we use the concept of proxemics. To model the social cost of invading areas where interaction is happening we include the concept of object interaction space. The framework uses Dijkstra’s algorithm on a uniform graph of free space where edges are weighed according to the social traversal cost of their outbound node. Experimental results demonstrate the validity of the proposal to plan socially-accepted paths.

Test-Retest Reliability of Kinematic Parameters of Timed Up and Go in People with Type 2 Diabetes

Francisco J. Dominguez-Muñoz, Miguel A. Hernández-Mocholi, Luis J. Manso, Daniel Collado-Mateo, Santos Villafaina, Jose C. Adsuar and Narcis Gusi
Journal Papers Applied Sciences, MDPI, ISSN 2076-3417, Vol 9, no. 21, (JCR 2.217), pp. 10. November 2019. DOI: 10.3390/app9214709. 2019.

BibTeX

@article{faicocho2019,
    author="Francisco J. Dominguez-Muñoz and Miguel A. Hernández-Mocholi and Luis J. Manso and Daniel Collado-Mateo and Santos Villafaina and Jose C. Adsuar and Narcis Gusi",
    title="Test-Retest Reliability of Kinematic Parameters of Timed Up and Go in People with Type 2 Diabetes",
    journal="Applied Sciences",
    year="2019",
    volume="9",
    number="21",
    pages="10",
    doi="https://doi.org/10.3390/app9214709",
}

Abstract

Diabetes mellitus is a chronic disease defined as a state of hyperglycaemia in fasting or postprandial states. Patients with type 2 diabetes mellitus (T2DM) often show reduced physical function, including low levels of strength, balance or mobility. In this regard, the timed up and go (TUG) is a widely used physical fitness test in people with T2DM. However, there is a lack of studies evaluating the properties TUG in this population. The present study aimed to evaluate the test-retest reliability of kinetic and kinematic parameters obtained from TUG in the diabetic population with different levels of diabetic neuropathy. A total of 56 patients with T2DM participated in the study. They were divided into three groups according to the vibration threshold: (a) severe neuropathy, (b) moderate neuropathy and (c) normal perception. The TUG was performed using two force platforms to assess kinematic measurements. The results show that both kinetic and kinematic variables had good to excellent reliability. The reliability of TUG was excellent for the whole sample and the groups with non-severe neuropathy. However, it was just good for the group with severe neuropathy.

The CORTEX Cognitive Robotics Architecture: use cases

P. Bustos, L.J. Manso, A.J. Bandera, J.P. Bandera, I. Garcia-Varea and J. Martinez-Gomez
Journal Papers Cognitive Systems Research, Science Direct, ISSN 1389-0417, Vol 55, (JCR 1.384), pp. 107-1237. January 2019. DOI: 10.1016/j.cogsys.2019.01.003. 2019.

BibTeX

@article{bustos2018thecortex,
    author="P. Bustos and L.J. Manso and A.J. Bandera and J.P. Bandera and I. Garcia-Varea and J. Martinez-Gomez",
    title="The CORTEX Cognitive Robotics Architecture: use cases",
    journal="Cognitive Systems Research",
    year="2019",
    volume="55",
    number="",
    pages="107-123",
    doi="https://doi.org/10.1016/j.cogsys.2019.01.003",
}

Abstract

CORTEX is a cognitive robotics architecture inspired by three key ideas: modularity, internal modelling and graph representations. CORTEX is also a computational framework designed to support early forms of intelligence in real world, human interacting robots, by selecting an a priori functional decomposition of the capabilities of the robot. This set of abilities was then translated to computational modules or agents, each one built as a network of software interconnected components. The nature of these agents can range from pure reactive modules connected to sensors and/or actuators, to pure deliberative ones, but they can only communicate with each other through a graph structure called Deep State Representation (DSR). DSR is a short-term dynamic representation of the space surrounding the robot, the objects and the humans in it, and the robot itself. All these entities are perceived and transformed into different levels of abstraction, ranging from geometric data to high-level symbolic relations such as ''the person is talking and gazing at me''. The combination of symbolic and geometric information endows the architecture with the potential to simulate and anticipate the outcome of the actions executed by the robot. In this paper we present recent advances in the CORTEX architecture and several real-world human-robot interaction scenarios in which they have been tested. We describe our interpretation of the ideas inspiring the architecture and the reasons why this specific computational framework is a promising architecture for the social robots of tomorrow.

Human-robot dialogue and Collaboration for social navigation in crowded environments

Carlos Lobato, Araceli Vega-Magro, Pedro Núñez and Luis J. Manso
Conference Papers 2019 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC). 2019.

BibTeX

@inproceedings{lobato2019hrdialogue,
    author="Carlos Lobato and Araceli Vega-Magro and Pedro Núñez and Luis J. Manso",
    title="Human-robot dialogue and Collaboration for social navigation in crowded environments",
    booktitle="Proceedings of 2019 IEEE International Conference on Autonomous Robot Systems and Competitions",
    year="2019",
    pages="6",
}

Abstract

Robot navigation in human-populated environments is a subject of great interest among the international scientific community. In order to be accepted in these scenarios, it is important for robots to navigate respecting social rules. Avoid getting too close to a person, not interrupting conversations or asking for permission or collaboration when it is required by social conventions, are some of the behaviours that robots must exhibit. This paper presents a social navigation system that integrates different software agents within a cognitive architecture for robots and describes, as the main contribution,the corpus that allows to establish dialogues between robots and humans in real situations to improve the human-aware navigation system. The corpus has been experimentally evaluated by the simulation of different daily situations, where robots need to plan interactions with real people. The results are analysed qualitatively, according to the behaviour expected by the robot in the interaction performed. The results show how the corpus presented in this paper improves the robot navigation, making it more socially accepted.

Socially aware robot navigation system in human-populated and interactive environments based on an adaptive spatial density function and space affordances

Araceli Vega, Luis J. Manso, Douglas G. Macharet, Pablo Bustos and Pedro Núñez
Journal Papers Pattern Recognition Letters, ISSN 0167-8655, Vol 2018, (JCR 1.952), pp. 13. August 2018. DOI: 10.1016/j.patrec.2018.07.015. 2019.

BibTeX

@article{vega2018socially,
    author="Araceli Vega and Luis J. Manso and Douglas G. Macharet and Pablo Bustos and Pedro Núñez",
    title="Socially aware robot navigation system in human-populated and interactive environments based on an adaptive spatial density function and space affordances",
    journal="Pattern Recognition Letters",
    year="2019",
    volume="2018",
    number="",
    pages="13",
    doi="https://doi.org/10.1016/j.patrec.2018.07.015",
}

Abstract

Traditionally robots are mostly known by society due to the wide use of manipulators, which are generally placed in controlled environments such as factories. However, with the advances in the area of mobile robotics, they are increasingly inserted into social contexts, i.e., in the presence of people. The adoption of socially acceptable behaviours demands a trade-off between social comfort and other metrics of efficiency. For navigation tasks, for example, humans must be differentiated from other ordinary objects in the scene. In this work, we propose a novel human-aware navigation strategy built upon the use of an adaptive spatial density function that efficiently cluster groups of people according to their spatial arrangement. Space affordances are also used for defining potential activity spaces considering the objects in the scene. The proposed function defines regions where navigation is either discouraged or forbidden. To implement a socially acceptable navigation, the navigation architecture combines a probabilistic roadmap and rapidly-exploring random tree path planners, and an adaptation of the elastic band algorithm. Trials in real and simulated environments carried out demonstrate that the use of the clustering algorithm and social rules in the navigation architecture do not hinder the navigation performance.

A Deep Evolutionary Approach to Bioinspired Classifier Optimisation for Brain-Machine Interaction

Jordan J. Bird, Diego R. Faria, Luis J. Manso, Anikó Ekárt and Christopher D. Buckingham
Journal Papers Complexity, ISSN 1099-0526, Vol 2019, (JCR 2.591), pp. 14. January 2019. DOI: https://doi.org/10.1155/2019/4316548. 2019.

BibTeX

@article{bird2019adeepevolutionary,
    author="Jordan J. Bird and Diego R. Faria and Luis J. Manso and Anikó Ekárt and Christopher D. Buckingham",
    title="A Deep Evolutionary Approach to Bioinspired Classifier Optimisation for Brain-Machine Interaction",
    journal="Complexity",
    year="2019",
    volume="2019",
    number="",
    pages="14",
    doi="https://doi.org/10.1155/2019/4316548",
}

Abstract

This study suggests a new approach to EEG data classification by exploring the idea of using evolutionary computation to both select useful discriminative EEG features and optimise the topology of Artificial Neural Networks. An evolutionary algorithm is applied to select the most informative features from an initial set of 2550 EEG statistical features. Optimisation of a Multilayer Perceptron (MLP) is performed with an evolutionary approach before classification to estimate the best hyperparameters of the network. Deep learning and tuning with Long Short-Term Memory (LSTM) are also explored, and Adaptive Boosting of the two types of models is tested for each problem. Three experiments are provided for comparison using different classifiers: one for attention state classification, one for emotional sentiment classification, and a third experiment in which the goal is to guess the number a subject is thinking of. The obtained results show that an Adaptive Boosted LSTM can achieve an accuracy of 84.44%, 97.06%, and 9.94% on the attentional, emotional, and number datasets, respectively. An evolutionary-optimised MLP achieves results close to the Adaptive Boosted LSTM for the two first experiments and significantly higher for the number-guessing experiment with an Adaptive Boosted DEvo MLP reaching 31.35%, while being significantly quicker to train and classify. In particular, the accuracy of the nonboosted DEvo MLP was of 79.81%, 96.11%, and 27.07% in the same benchmarks. Two datasets for the experiments were gathered using a Muse EEG headband with four electrodes corresponding to TP9, AF7, AF8, and TP10 locations of the international EEG placement standard. The EEG MindBigData digits dataset was gathered from the TP9, FP1, FP2, and TP10 locations.

Planning Human-Robot Interaction for Social Navigation in Crowded Environments

A. Vega-Magro, L.J. Manso, P. Bustos and P. Núñez
Book Chapters WAF 2018: Advances in Physical Agents, pp. 195-208, Springer. 2018.

BibTeX

@inbook{vega2018planninghri,
    author="A. Vega-Magro and L.J. Manso and P. Bustos and P. Núñez",
    title="Planning Human-Robot Interaction for Social Navigation in Crowded Environments",
    pages="195-208",
    publisher="Springer",
    year="2018",
    volume="855",
    type="bookchapter",
    isbn="978-3-319-99884-8",
}

Abstract

Navigation is one of the crucial skills autonomous robots need to perform daily tasks, and many of the rest depend on it. In this paper, we argue that this dependence goes both ways in advanced social autonomous robots. Manipulation, perception, and most importantly human-robot interaction are some of the skills in which navigation might rely on. This paper is focused on the dependence on human-robot interaction and uses two particular scenarios of growing complexity as an example: asking for collaboration to enter a room and asking for permission to navigate between two people which are talking. In the first scenario, the person physically blocks the path to the adjacent room, so it would be impossible for the robot to navigate to such room. Even though in the second scenario the people talking do not block the path to the other room, from a social point of view, interrupting an ongoing conversation without noticing is undesirable. In this paper we propose a navigation planning domain and a set of software agents which allow the robot to navigate in crowded environments in a socially acceptable way, asking for cooperation or permission when necessary. The paper provides quantitative experimental results including social navigation metrics and the results of a Likert-scale satisfaction questionnaire.

A Flexible and Adaptive Spatial Density Model for Context-Aware Social Mapping: Towards a More Realistic Social Navigation

Araceli Vega-Magro, Luis J. Manso, P. Bustos and P. Núñez
Conference Papers Proceedings of 15th International Conference on Control, Automation, Robotics and Vision (ICARCV), pp. 1727-1732, 2018.

BibTeX

@inproceedings{vega2018aflexible,
    author="Araceli Vega-Magro and Luis J. Manso and P. Bustos and P. Núñez",
    title="A Flexible and Adaptive Spatial Density Model for Context-Aware Social Mapping: Towards a More Realistic Social Navigation",
    booktitle="Proceedings of 15th International Conference on Control, Automation, Robotics and Vision",
    year="2018",
    pages="1727--1732",
}

Abstract

Social navigation is a topic with enormous interest in autonomous robotics. Robots are gradually being used in human environments, working individually or collaborating with humans in their daily tasks. Robots in these scenarios have to be able to behave in a socially acceptable way and, for this reason, the way in which robots move has to adapt to humans and context. Proxemics has been extensively studied with the aim of improving social navigation. However, these works do not take into account that, in several situations, the personal space of the humans depends on the context (e.g., this human space is not the same in a narrow corridor than in a wide room). This work proposes the definition of an adaptive and flexible space density function that allows, on the one hand, to describe the comfort space of individuals during an interaction and, on the other hand, dynamically adapt its value in terms of the space that surrounds this interaction. In order to validate the performance, this article describes a set of simulated experiments where the robustness and improvements of the approach are tested in different environments.

Multimodal Bayesian Network for Artificial Perception

Diego R. Faria, Cristiano Prembeida, Luis J. Manso, Eduardo P. Ribeiro and P. Nunez
Book Chapters Bayesian Networks, pp 17, IntechOpen, 2018.

BibTeX

@inbook{faria2018multimodal,
    author="Diego R. Faria and Cristiano Prembeida and Luis J. Manso and Eduardo P. Ribeiro and P. Nunez",
    title="Multimodal Bayesian Network for Artificial Perception",
    pages="17",
    publisher="IntechOpen",
    year="2018",
    volume="1",
    editor="IntechOpen",
    type="bookchapter",
    doi="10.5772/intechopen.81111",
    isbn="",
}

Abstract

In order to make machines perceive their external environment coherently, multiple sources of sensory information derived from several different modalities can be used (eg cameras, LIDAR, stereo, RGB-D, and radars). All these different sources of information can be efficiently merged to form a robust perception of the environment. Some of the mechanisms that underlie this merging of the sensor information are highlighted in this chapter, showing that depending on the type of information, different combination and integration strategies can be used and that prior knowledge are often required for interpreting the sensory signals efficiently. The notion that perception involves Bayesian inference is an increasingly popular position taken by a considerable number of researchers. Bayesian models have provided insights into many perceptual phenomena, showing that they are a valid approach to deal with real-world uncertainties and for robust classification, including classification in time-dependent problems. This chapter addresses the use of Bayesian networks applied to sensory perception in the following areas: mobile robotics, autonomous driving systems, advanced driver assistance systems, sensor fusion for object detection, and EEG-based mental states classification.

A study on mental state classification using eeg-based brain-machine interface

Jordan J. Bird, Luis J. Manso, Eduardo P. Ribeiro, Aniko Ekart and Diego R. Faria
Conference Papers 2018 International Conference on Intelligent Systems (IS). 2018.

BibTeX

@inproceedings{bird2018astudyonmental,
    author="Jordan J. Bird and Luis J. Manso and Eduardo P. Ribeiro and Aniko Ekart and Diego R. Faria",
    title="A study on mental state classification using eeg-based brain-machine interface",
    booktitle="Proceedings of 2018 International Conference on Intelligent Systems",
    year="2018",
    pages="795-800",
}

Abstract

This work aims to find discriminative EEG-based features and appropriate classification methods that can categorise brainwave patterns based on their level of activity of frequency for mental state recognition useful for human-machine interaction. By using the Muse headband with four EEG sensors (TP9, AF7, AF8, TP10), we categorised three possible states such as relaxing, neutral and concentrating based on a few states of mind defined by cognitive behavioural studies. We have created a dataset with five individuals and sessions lasting one minute for each class of mental state in order to train and test different methods. Given the proposed set of features extracted from the EEG headband five signals (alpha, beta, theta, delta, gamma), we have tested a combination of different features selection algorithms and classifier models to compare their performance in terms of recognition accuracy and number of features needed. Different tests such as 10-fold cross validation were preformed. Results show that only 44 features from a set of over 2100 features are necessary when used with classical classifiers such as Bayesian Networks, Support Vector Machines and Random Forests, attaining an overall accuracy over 87%.

Emotion Recognition using Spatiotemporal Features from Facial Expression Landmarks

Hamid Golzadeh, Diego R. Faria, Luis J. Manso, Aniko Ekart and Christopher D. Buckingham
Conference Papers 2018 International Conference on Intelligent Systems (IS), 2018.

BibTeX

@inproceedings{golzadeh2018emotion,
    author="Hamid Golzadeh and Diego R. Faria and Luis J. Manso and Aniko Ekart and Christopher D. Buckingham",
    title="Emotion Recognition using Spatiotemporal Features from Facial Expression Landmarks",
    booktitle="Proceedings of 2018 International Conference on Intelligent Systems",
    year="2018",
    pages="789-794",
}

Abstract

Emotion expression is a type of nonverbal communication (i.e. wordless cues) between people, where affect plays the role of interpersonal communication with information conveyed by facial and/or body expressions. Much can be understood about how people are feeling through their expressions, which are crucial for everyday communication and interaction. This paper presents a study on spatiotemporal feature extraction based on tracked facial landmarks. The features are tested with multiple classification methods to verify whether they are discriminative enough for an automatic emotion recognition system. The Karolinska Directed Emotional Faces (KDEF) [1] were used to determine features representing the human facial expressions of angry, disgusted, happy, sad, afraid, surprised and neutral. The resulting set of features were tested using K-fold cross-validation. Experimental results show that facial expressions can be recognised correctly with an accuracy of upt o 87% when using the newly-developed features and a multiclass Support Vector Machine classifier.

A Spiking Neural Model of HT3D for Corner Detection

Pilar Bachiller, Luis J. Manso and Pablo Bustos
Journal Papers Frontiers in Computational Neuroscience ISSN 1662-5188. Vol. 2018. (JCR 1.714 Q2), pp. 21, June 2018. DOI: 10.3389/fncom.2018.00037. 2018.

BibTeX

@article{bachiller2018aspiking,
    author="Pilar Bachiller and Luis J. Manso and Pablo Bustos",
    title="A Spiking Neural Model of HT3D for Corner Detection",
    journal="Frontiers in Computational Neuroscience",
    year="2018",
    volume="12",
    number="37",
    pages="to-be-published",
    doi="10.3389/fncom.2018.00037 ",
}

Abstract

Obtaining good quality image features is of remarkable importance for most computer vision tasks. It has been demonstrated that the first layers of the human visual cortex are devoted to feature detection. The need for these features has made line, segment, and corner detection one of the most studied topics in computer vision. HT3D is a recent variant of the Hough transform for the combined detection of corners and line segments in images. It uses a 3D parameter space that enables the detection of segments instead of whole lines. This space also encloses canonical configurations of image corners, transforming corner detection into a pattern search problem. Spiking neural networks have previously been proposed for multiple image processing tasks, including corner and line detection using the Hough transform. Following these ideas, this paper presents and describes in detail a model to implement HT3D as a Spiking Neural Network for corner detection. The results obtained from a thorough testing of its implementation using real images evince the correctness of the Spiking Neural Network HT3D implementation. Such results are comparable to those obtained with the regular HT3D implementation, which are turn superior to other corner detection algorithms.

Planning Object Informed Search for Robots in Household Environments

M.A. Gutierrez, L.J. Manso, P. Núñez and P. Bustos
Conference Papers Proceedings of IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC 2018), pp. (to appear), 2018.

BibTeX

@inproceedings{gutierrez2018planning,
    author="M.A. Gutierrez and L.J. Manso and P. Núñez and P. Bustos",
    title="Planning Object Informed Search for Robots in Household Environments",
    booktitle="Proceedings of IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC 2018))",
    year="2018",
    pages="205-210",
}

Abstract

In the current state-of-the-art, social robots performing non-trivial tasks often spend most of their time finding and modeling objects. In this paper we present the extension of a cognitive architecture that reduces the time and effort a robot needs to retrieve objects in a household scenario. We upgrade our previous Passive Learning Sensor algorithm into a full fledged agent that is part of the CORTEX robotics cognitive architecture. With its planning capabilities, this new configuration allows the robot to efficiently search, pick and deliver different objects from different locations in large households environments. The contribution presented here dynamically extends the robot's knowledge of the world by making use of memories from past experiences. Results obtained from several experiments show that, both, the new software agent and the integrated cognitive architecture, constitute an important step towards robot autonomy. The experiments show that the find-and-pick task is greatly accelerated.

CLARC: A cognitive robot for helping geriatric doctors in real scenarios

Dimitri Voilmy, Cristina Suárez, Adrian Romero-Garcés, Cristian Reuther, Jose Carlos Pulido, Rebeca Marfil, Luis J Manso, Karine Lan Hing Ting, Ana Iglesias, Jose Carlos González, Javier García, Angel García-Olaya, Raquel Fuentetaja, Fernando Fernández, Alvaro Dueñas, Luis Vicente Calderita, Pablo Bustos, T Barile, Juan Pedro Bandera and Antonio Bandera
Conference Papers Proceedings of Iberian Robotics conference 2017, ISBN 978-3-319-70833-1, pp 403-414, 2017.

BibTeX

@inproceedings{voilmy2017clarc,
    author="Dimitri Voilmy and Cristina Suárez and Adrian Romero-Garcés and Cristian Reuther and Jose Carlos Pulido and Rebeca Marfil and Luis J Manso and Karine Lan Hing Ting and Ana Iglesias and Jose Carlos González and Javier García and Angel García-Olaya and Raquel Fuentetaja and Fernando Fernández and Alvaro Dueñas and Luis Vicente Calderita and Pablo Bustos and T Barile and Juan Pedro Bandera and Antonio Bandera",
    title="CLARC: A cognitive robot for helping geriatric doctors in real scenarios",
    booktitle="Proceedings of Iberian Robotics conference 2017",
    year="2017",
    pages="403-414",
}

Abstract

Comprehensive Geriatric Assessment (CGA) is an integrated clinical process to evaluate the frailty of elderly persons in order to create therapy plans that improve their quality of life. For robotizing these tests, we are designing and developing CLARC, a mobile robot able to help the physician to capture and manage data during the CGA procedures, mainly by autonomously conducting a set of predefined evaluation tests. Built around a shared internal representation of the outer world, the architecture is composed of software modules able to plan and generate a stream of actions, to execute actions emanated from the representation or to update this by including/removing items at different abstraction levels. Percepts, actions and intentions coming from all software modules are grounded within this unique representation. This allows the robot to react to unexpected events and to modify the course of action according to the dynamics of a scenario built around the interaction with the patient. The paper describes the architecture of the system as well as the preliminary user studies and evaluation to gather new user requirements.

LifeBots I: Building the Software Infrastructure for Supporting Lifelong Technologies

Antonio Bandera, Juan P. Bandera, Pablo Bustos, Fernando Férnandez, Angel García-Olaya, Javier García-Polo, Ismael García-Varea, Luis J. Manso, Rebeca Marfil, Jesús Martínez-Gómez, Pedro Núñez, Jose M. Perez-Lorenzo, Pedro Reche-Lopez, Cristina Romero-González and Raquel Viciana-Abad
Conference Papers Proceedings of Iberian Robotics conference 2017, ISBN 978-3-319-70833-1, pp 391-402, 2017.

BibTeX

@inproceedings{bandera2017lifebots,
    author="Antonio Bandera and Juan P. Bandera and Pablo Bustos and Fernando Férnandez and Angel García-Olaya and Javier García-Polo and Ismael García-Varea and Luis J. Manso and Rebeca Marfil and Jesús Martínez-Gómez and Pedro Núñez and Jose M. Perez-Lorenzo and Pedro Reche-Lopez and Cristina Romero-González and Raquel Viciana-Abad",
    title="LifeBots I: Building the Software Infrastructure for Supporting Lifelong Technologies",
    booktitle="Proceedings of IEEE Intern",
    year="2017",
    pages="391-402",
}

Abstract

The goal of the LifeBots project is the study and development of long-life mechanisms that facilitate and improve the integration of robotics platforms in smart homes to support elder and handicapped people. Specifically the system aims to design, build and validate an assistive ecosystem formed by a person living in a smart home with a social robot as her main interface to a gentler habitat. Achieving this goal requires the use and integration of different technologies and research areas, but also the development of the mechanisms in charge of providing an unified, pro-active response to the user’s needs. This paper describes some of the mechanisms implemented within the cognitive robotics architecture CORTEX that integrates deliberative and reactive agents through a common understanding and internalizing of the outer reality, which materializes in a shared representation derived from a formal graph grammar.

Integrating planning perception and action for informed object search

Luis J. Manso, Marco A. Gutierrez, Pablo Bustos and Pilar Bachiller
Journal Papers Cognitive Processing ISSN 1612-4782. Vol. 2017. (JCR 0.974 Q3), pp 1-12, August 2017. DOI: http://doi.org/10.1007/s10339-017-0828-3. 2017.

BibTeX

@article{manso2017integrating,
    author="Luis J. Manso and Marco A. Gutierrez and Pablo Bustos and Pilar Bachiller",
    title="Integrating planning perception and action for informed object search",
    journal="Cognitive Processing",
    year="2017",
    volume="2017",
    number="",
    pages="1-12",
    doi="https://doi.org/10.1007/s10339-017-0828-3",
}

Abstract

This paper presents a method to reduce the time spent by a robot with cognitive abilities when looking for objects in unknown locations. It describes how machine learning techniques can be used to decide which places should be inspected first, based on images that the robot acquires passively. The proposal is composed of two concurrent processes. The first one uses the aforementioned images to generate a description of the types of objects found in each object container seen by the robot. This is done passively, regardless of the task being performed. The containers can be tables, boxes, shelves or any other kind of container of known shape whose contents can be seen from a distance. The second process uses the previously computed estimation of the contents of the containers to decide which is the most likely container having the object to be found. This second process is deliberative and takes place only when the robot needs to find an object, whether because it is explicitly asked to locate one or because it is needed as a step to fulfil the mission of the robot. Upon failure to guess the right container, the robot can continue making guesses until the object is found. Guesses are made based on the semantic distance between the object to find and the description of the types of the objects found in each object container. The paper provides quantitative results comparing the efficiency of the proposed method and two base approaches.

Use and advances in the Active Grammar-based Modeling architecture

L.J. Manso, L.V. Calderita, P. Bustos and A. Bandera
Journal Papers Journal of Physical Agents ISSN 1888-0258. Vol. 8, No. 1, pp 34-39. July 2017. DOI: 10.14198/JoPha. 2017.

BibTeX

@article{manso2017useandadvancesj,
    author="L.J. Manso and L.V. Calderita and P. Bustos and A. Bandera",
    title="Use and advances in the Active Grammar-based Modeling architecture",
    journal="Journal of Physical Agents",
    year="2017",
    volume="8",
    number="1",
    doi="DOI: 10.14198/JoPha",
}

Abstract

The choice of using a robotic architecture and one of its possible implementations is one of the most crucial design decisions when developing robots. Such decision affects the whole development process, the limitations of the robot, and changing minds can be prohibitively time consuming. This paper presents the redesign and the most relevant implementation issues of the Active Grammar-based Modeling architecture (AGM), as well as the latest developments thereof. AGM is flexible, modular and designed with computation distribution and scalability in mind. In addition to a continuous refactoring of the API library and planner, the most relevant improvements are an enhanced mission specification syntax, support for representations combining symbolic and metric properties, redesigned communication patterns, and extended middleware support. A few use examples are presented to demonstrate successful application of the architecture and why some of its features were needed.

A variant of the Hough Transform for the combined detection of corners, segments, and polylines

Pilar Bachiller, Luis J. Manso and Pablo Bustos
Journal Papers EURASIP Journal on Image and Video Processing 2017, 2017(32) doi: 10.1186/s13640-017-0180-7 (JCR 1.742 Q2). 2017.

BibTeX

@article{bachiller2017variant,
    author="Pilar Bachiller and Luis J. Manso and Pablo Bustos",
    title="A variant of the Hough Transform for the combined detection of corners, segments, and polylines",
    journal="EURASIP Journal on Image and Video Processing",
    year="2017",
    volume="2017",
    number="32",
    doi="10.1186/s13640-017-0180-7",
}

Abstract

The Hough Transform (HT) is an effective and popular technique for detecting image features such as lines and curves. From its standard form, numerous variants have emerged with the objective, in many cases, of extending the kind of image features that could be detected. Particularly, corner and line segment detection using HT has been separately addressed by several approaches. To deal with the combined detection of both image features (corners and segments), this paper presents a new variant of the Hough Transform. The proposed method provides an accurate detection of segment endpoints, even if they do not correspond to intersection points between line segments. Segments are detected from their endpoints, producing not only a set of isolated segments but also a collection of polylines. This provides a direct representation of the polygonal contours of the image despite imperfections in the input data such as missing or noisy feature points. It is also shown how this proposal can be extended to detect predefined polygonal shapes. The paper describes in detail every stage of the proposed method and includes experimental results obtained from real images showing the benefits of the proposal in comparison with other approaches.

A Passive Learning Sensor Architecture for Multimodal Image Labeling: An Application for Social Robots

Marco A. Gutierrez, Luis J. Manso, Harit Pandya and Pedro Nunez
Journal Papers Sensors 2017, 17(2), 353; doi:10.3390/s17020353. (JCR 2.677 Q1), 2017.

BibTeX

@article{gutierrez2017apassive,
    author="Marco A. Gutierrez and Luis J. Manso and Harit Pandya and Pedro Nunez",
    title="A Passive Learning Sensor Architecture for Multimodal Image Labeling: An Application for Social Robots",
    journal="Sensors",
    year="2017",
    volume="17",
    number="2",
    doi="10.3390/s17020353",
}

Abstract

Object detection and classification have countless applications in human-robot interacting systems. It is a necessary skill for autonomous robots that perform tasks in household scenarios. Despite the great advances in deep learning and computer vision, social robots performing non-trivial tasks usually spend most of their time finding and modeling objects. Working in real scenarios means dealing with constant environment changes and relatively low-quality sensor data due to the distance at which objects are often found. Ambient intelligence systems equipped with different sensors can also benefit from the ability to find objects, enabling them to inform humans about their location. For these applications to succeed, systems need to detect the objects that may potentially contain other objects, working with relatively low-resolution sensor data. A passive learning architecture for sensors has been designed in order to take advantage of multimodal information, obtained using an RGB-D camera and trained semantic language models. The main contribution of the architecture lies in the improvement of the performance of the sensor under conditions of low resolution and high light variations using a combination of image labeling and word semantics. The tests performed on each of the stages of the architecture compare this solution with current research labeling techniques for the application of an autonomous social robot working in an apartment. The results obtained demonstrate that the proposed sensor architecture outperforms state-of-the-art approaches.

Socially Acceptable Robot Navigation over Groups of People

A. Vega, L.J. Manso, P. Bustos, P. Núñez and D.G. Macharet
Conference Papers Proceedings of IEEE International Conference on Robot and Human Interactive Communication (RO-MAN 2017), pp. 1182-1187, 2017.

BibTeX

@inproceedings{vega2017socially,
    author="A. Vega and L.J. Manso and P. Bustos and P. Núñez and D.G. Macharet",
    title="Socially Acceptable Robot Navigation over Groups of People",
    booktitle="Proceedings of IEEE International Conference on Robot and Human Interactive Communication (RO-MAN 2017))",
    year="2017",
    pages="1182-1187",
}

Abstract

Considering the widespread use of mobile robots in different parts of society, it is important to provide them with the capability to behave in a socially acceptable manner. Therefore, a research topic of great importance recently has been the study of Human-Robot Interaction. Autonomous navigation is a fundamental task in Robotics, and several different strategies that produce paths that are either length or time optimized can be found in the literature. However, considering the recent use of mobile robots in a more social context, the use of such classical techniques is restricted. Therefore, in this article we present a social navigation approach considering environments with groups of people. The proposal uses a density function to efficiently represent groups of people, and modify the navigation architecture in order to include the social behaviour of the robot during its motion. This architecture is based on the combined use of the Probabilistic Road Mapping (PRM) and the Rapidly-exploring Random Tree (RRT) path planners and an adaptation of the elastic band algorithm. Experimental evaluation was carried out in different simulated environments, providing insight on the performance of the proposed technique, which surpasses classical techniques with no proxemics awareness in terms of social impact.

A Perception-aware Architecture for Autonomous Robots

L.J. Manso, P. Bustos, P. Bachiller and P. Núñez
Journal Papers International Journal of Advanced Robotic Systems (ISSN 1729-8806), InTech, Vol. 12, No. 174, 2015. DOI: 10.5772/61742 (JCR 0.615 Q3). 2015.

BibTeX

@article{manso2015perceptionaware,
    author="L.J. Manso and P. Bustos and P. Bachiller and P. Núñez",
    title="A Perception-aware Architecture for Autonomous Robots",
    journal="International Journal of Advanced Robotic Systems",
    year="2015",
    volume="12",
    number="174",
    pages="13",
    doi="DOI: 10.5772/61742",
}

Abstract

Planning Human-Robot Interaction Tasks using Graph Models

L.J. Manso, P. Bustos, R. Alami, G. Milliez and P. Núñez
Conference Papers Proceedings of International Workshop on Recognition and Action for Scene Understanding (REACTS 2015), pp. 15-27, 2015.

BibTeX

@inproceedings{manso2015planning,
    author="L.J. Manso and P. Bustos and R. Alami and G. Milliez and P. Núñez",
    title="Planning Human-Robot Interaction Tasks using Graph Models",
    booktitle="Proceedings of International Workshop on Recognition and Action for Scene Understanding (REACTS 2015)",
    year="2015",
    pages="15--27",
}

Abstract

A Novel Multimodal Emotion Recognition Approach for Affective Human Robot Interaction

F. Cid, L.J. Manso and Pedro Nunez
Conference Papers Proceedings of the Workshop on Multimodal and Semantics for Robotics Systems (MuSRobS @ IEEE/RSJ International Conference on Intelligent Robots and Systems 2015), pp. 1-9, ISSN 1613-0073, 2015

BibTeX

@inproceedings{cid2015facial,
    author="F. Cid and L.J. Manso and Pedro Nunez",
    title="A Novel Multimodal Emotion Recognition Approach for Affective Human Robot Interaction",
    booktitle="Proceedings of the Workshop on Multimodal and Semantics for Robotics Systems",
    year="2015",
    pages="1-9",
}

Abstract

Facial expressions and speech are elements that provide emotional information about the user through multiple communication channels. In this paper, a novel multimodal emotion recognition system based on visual and auditory information processing is proposed. The proposed approach is used in real affective human robot communication in order to estimate five different emotional states (i.e., happiness, anger, fear, sadness and neutral), and it consists of two subsystems with similar structure. The first subsystem achieves a robust facial feature extraction based on consecutively applied filters to the edge image and the use of a Dynamic Bayessian Classifier. A similar classifier is used in the second subsystem, where the input is associated to a set of speech descriptors, such as speech-rate, energy and pitch. Both subsystems are finally combined in real time. The results of this multimodal approach show the robustness and accuracy of the methodology respect to single emotion recognition systems.

RGBD Data analysis for Real-Time Emotion Recognition from Upper Body Movements

F. Cid, E. Mogena, L.J. Manso and P. Núñez
Conference Papers Proceedings of International Workshop on Recognition and Action for Scene Understanding (REACTS 2015), pp. 43-52, 2015

BibTeX

@inproceedings{cid2015rgbd,
    author="F. Cid and E. Mogena and L.J. Manso and P. Núñez",
    title="RGBD Data analysis for Real-Time Emotion Recognition from Upper Body Movements",
    booktitle="Proceedings of International Workshop on Recognition and Action for Scene Understanding (REACTS 2015)",
    year="2015",
    pages="43-52",
}

Abstract

Testing a fully autonomous robotic salesman in real scenarios

A. Romero, L.V. Calderita, J. Martín, J.P. Bandera, R. Marfil, L.J. Manso, A. Bandera and P. Bustos
Conference Papers Proceedings of the IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC 2015), IEEE, pp. 124-130, ISBN 978-1-4673-6991-6, 2015

BibTeX

@inproceedings{romero2015testing,
    author="A. Romero and L.V. Calderita and J. Martín and J.P. Bandera and R. Marfil and L.J. Manso and A. Bandera and P. Bustos",
    title="Testing a fully autonomous robotic salesman in real scenarios",
    booktitle="Proceedings of International Conference on Autonomous Robot Systems and Competitions (ICARSC 2015)",
    year="2015",
    editor="IEEE",
    pages="124-130",
}

Abstract

THERAPIST: Towards an Autonomous Socially Interactive Robot for Motor and Neurorehabilitation Therapies for Children

L.V. Calderita, L.J. Manso, P. Bustos, C. Suárez, F. Fernández and A. Bandera
Journal Papers Journal of Rehabilitation and Assistive Technologies, JMIR, vol. 1, n. 1, DOI:10.2196/rehab.31512014, 2014

BibTeX

@article{calderita2014therapist,
    author="L.V. Calderita and L.J. Manso and P. Bustos and C. Suárez and F. Fernández and A. Bandera",
    title="THERAPIST: Towards an Autonomous Socially Interactive Robot for Motor and Neurorehabilitation Therapies for Children",
    journal="Journal of Rehabilitation and Assistive Technologies, JMIR",
    year="2014",
    volume="1",
    number="1",
    doi="10.2196/rehab.31512014",
}

Abstract

Toward Social Cognition in Robotics: Extracting and Internalizing Meaning from Perception

J. Martínez, R. Marfil, L.V. Calderita, J.P. Bandera, L.J Manso, A. Bandera, A. Romero and P. Bustos
Conference Papers Proceedings of Workshop of Physical Agents (WAF), pp. 93-104, ISBN ISBN: 978-84-9773-681-7, 2014

BibTeX

@inproceedings{martinez2014toward,
    author="J. Martínez and R. Marfil and L.V. Calderita and J.P. Bandera and L.J Manso and A. Bandera and A. Romero and P. Bustos",
    title="Toward Social Cognition in Robotics: Extracting and Internalizing Meaning from Perception",
    booktitle="Proceedings of Workshop of Physical Agents",
    year="2014",
    pages="",
}

Abstract

A General-Purpose Architecture to Control Mobile Robots

L.J. Manso, L.V. Calderita, P. Bustos, J. García, M. Martínez, F. Fernández, A. Romero and A. Bandera
Conference Papers In Proceedings of Workshop of Physical Agents (WAF), pp. 105-116, 2014

BibTeX

@inproceedings{manso2014generalpurpose,
    author="L.J. Manso and L.V. Calderita and P. Bustos and J. García and M. Martínez and F. Fernández and A. Romero and A. Bandera",
    title="A General-Purpose Architecture to Control Mobile Robots",
    booktitle="Proceedings of Workshop of Physical Agents (WAF)",
    year="2014",
    pages="105--116",
}

Abstract

A Multi-layer Description of the Environment using Curvature Information for Robot Navigation

J. Almeida, L.J. Manso, A. Bandera and P. Núñez
Conference Papers Proceedings of Workshop of Physical Agents (WAF), pp. 135-144, 2014

BibTeX

@inproceedings{almeida2014multilayer,
    author="J. Almeida and L.J. Manso and A. Bandera and P. Núñez",
    title="A Multi-layer Description of the Environment using Curvature Information for Robot Navigation",
    booktitle="Proceedings of Workshop of Physical Agents (WAF)",
    year="2014",
    pages="135--144",
}

Abstract

A Novel Robust Scene Change Detection Algorithm for Autonomous Robots using Mixtures of Gaussians

Luis J. Manso, Pedro Núñez, Sidnei Da Silva and Paulo Drews-Jr
Journal Papers International Journal of Advanced Robotic Systems (ISSN 1729-8806), InTech, vol. 11, no. 18, 2014 (JCR 0.497 Q3). 2014.

BibTeX

@article{manso2014novel,
    author="Luis J. Manso and Pedro Núñez and Sidnei Da Silva and Paulo Drews-Jr",
    title="A Novel Robust Scene Change Detection Algorithm for Autonomous Robots using Mixtures of Gaussians",
    journal="International Journal of Advanced Robotic Systems (ISSN 1729-8806)",
    year="2014",
    volume="11",
    number="18",
    doi="10.5772/57360",
}

Abstract

Improving Change Detection using Vertical Surface Normal Histograms and Gaussian Mixture Models in Structured Environments

Paulo Drews-Jr, Luis J. Manso, Sidnei Da Silva and Pedro Núñez
Conference Papers Proc. of The 16th International Conference on Advanced Robotics (ICAR), ISBN:978-1-4799-2722-7, 25 November, 2013.

BibTeX

@inproceedings{drews2013improving,
    author="Paulo Drews-Jr and Luis J. Manso and Sidnei Da Silva and Pedro Núñez",
    title="Improving Change Detection using Vertical Surface Normal Histograms and Gaussian Mixture Models in Structured Environments",
    booktitle="Proc. of The 16th International Conference on Advanced Robotics",
    year="2013",
}

Abstract

Multimodal interaction with Loki

P. Bustos, J. Martinez, I. Garcia, L. Rodriguez, P. Bachiller, L. Calderita, L.J. Manso, A. Sanchez, A. Bandera and J.P. Bandera
Conference Papers Proc. of Workshop of Physical Agents, pages 53--60, 2013.

BibTeX

@inproceedings{bustos2013multimodal,
    author="P. Bustos and J. Martinez and I. Garcia and L. Rodriguez and P. Bachiller and L. Calderita and L.J. Manso and A. Sanchez and A. Bandera and J.P. Bandera",
    title="Multimodal interaction with Loki",
    booktitle="Proc. of Workshop of Physical Agents",
    year="2013",
    pages="53--60",
}

Abstract

Perception as Stochastic Sampling on Dynamic Graph Spaces

L.J. Manso
Theses PhD. Thesis. Cáceres School of Technology, University of Extremadura, 2013.

BibTeX

@phdthesis{manso2013perception,
    author="L.J. Manso",
    title="Perception as Stochastic Sampling on Dynamic Graph Spaces",
    school="Cáceres School of Technology, University of Extremadura",
    year="2013",
    type="phdthesis",
}

Abstract

Ursus: A robotic assistant for training of children with motor impairments

C. Suarez-Mejías, C. Echevarría, P. Núñez, L.J. Manso, P. Bustos, S. Leal and C. Parra
Book Chapters Converging Clinical and Engineering Research on Neurorehabilitation Biosystems & Biorobotics, pp. 249--253, Springer, 2013.

BibTeX

@inbook{suarez2013ursus,
    author="C. Suarez-Mejías and C. Echevarría and P. Núñez and L.J. Manso and P. Bustos and S. Leal and C. Parra",
    title="Ursus: A robotic assistant for training of children with motor impairments",
    pages="249-253",
    publisher="Springer",
    year="2013",
    volume="1",
    editor="Springer",
    type="bookchapter",
    isbn="978-3-642-34545-6",
}

Abstract

Experiments in self-calibration of an autonomous mobile manipulator

A. Sánchez, P. Núñez, L.J. Manso and P. Bustos
Conference Papers Proc. of Workshop of Physical Agents, pp. 183--190, 2012.

BibTeX

@inproceedings{sanchez2012experiments,
    author="A. Sánchez and P. Núñez and L.J. Manso and P. Bustos",
    title="Experiments in self-calibration of an autonomous mobile manipulator",
    booktitle="Proc. of Workshop of Physical Agents",
    year="2012",
    pages="183-190",
}

Abstract

Indoor scene perception for object detection and manipulation

L.J. Manso, P. Bustos, P. Bachiller and J. Franco
Journal Papers Journal of Cognitive Processing. Vol. 13. No. 1. pp. 55-56 (JCR:1.754), DOI 10.1007/s10339-012-0509-1. Springer. 2012.

BibTeX

@article{manso2012indoor,
    author="L.J. Manso and P. Bustos and P. Bachiller and J. Franco",
    title="Indoor scene perception for object detection and manipulation",
    journal="Symposium on Spatial Cognition in Robotics. International Conference on Spatial Cognition",
    year="2012",
    volume="13",
    number="1",
    pages="55-56",
}

Abstract

Graph Grammars for Active Perception

Luis J. Manso, Pablo Bustos, Pilar Bachiller and Marco A. Gutierrez
Conference Papers Proc. of 12th International Conference on Autonomous Robot Systems and Competitions, pp.63--68, (ISBN 978-972-98603-4-8). 2012.

BibTeX

@inproceedings{manso2012graph,
    author="Luis J. Manso and Pablo Bustos and Pilar Bachiller and Marco A. Gutierrez",
    title="Graph Grammars for Active Perception",
    booktitle="Proc. of 12th International Conference on Autonomous Robot Systems and Competitions",
    year="2012",
    pages="63-68",
}

Abstract

Engaging human-to-robot attention using conversational gestures and lip-synchronization

F. Cid, R. Cintas, L.J. Manso, L. Calderita, A. Sánchez and P Núñez
Journal Papers Journal of Physical Agents ISSN 1888-0258. Vol. 6, No. 1, pp 3-10. March 2012.

BibTeX

@article{cid2012engaging,
    author="F. Cid and R. Cintas and L.J. Manso and L. Calderita and A. Sánchez and P Núñez",
    title="Engaging human-to-robot attention using conversational gestures and lip-synchronization",
    journal="Journal of Physical Agents",
    year="2012",
    volume="6",
    number="1",
    pages="3-10",
}

Abstract

Interactive games with robotic and augmented reality technology in cognitive and motor rehabilitation

A.B. Naranjo, C. Suárez, C. Parra, E. González, F. Bockel, A. Yuste, P. Bustos, L. Manso, P. Bachiller and S. Plana
Book Chapters Serious Games as Educational, Business, and Research Tools: Development and Design. ISBN 978-1-4666-0149-9. Vol. 1, pp. 1212-1233. February 2012.

BibTeX

@inbook{naranjo2012interactive,
    author="A.B. Naranjo and C. Suárez and C. Parra and E. González and F. Bockel and A. Yuste and P. Bustos and L. Manso and P. Bachiller and S. Plana",
    title="Interactive games with robotic and augmented reality technology in cognitive and motor rehabilitation",
    pages="1212-1233",
    publisher="IGI Global",
    year="2012",
    volume="1",
    editor="IGI Global",
    type="bookchapter",
    isbn="978-1-4666-0149-9",
}

Abstract

Improving the life cycle of robotics components using Domain Specific Languages

A. Romero, L.J. Manso, M.A. Gutiérrez, R. Cintas and P. Bustos
Conference Papers 2nd International Workshop on Domain-Specific Languages and models for ROBotic systems (DSLRob'2011). September 2011.

BibTeX

@inproceedings{romero2011improving,
    author="A. Romero and L.J. Manso and M.A. Gutiérrez and R. Cintas and P. Bustos",
    title="Improving the life cycle of robotics components using Domain Specific Languages",
    booktitle="Proc. of Int. Workshop on Domain-Specific Languages and models for ROBotic systems (DSLRob'2011)",
    year="2011",
    pages="1--9",
}

Abstract

An incremental hybrid approach to indoor modeling

P. Bachiller, M.A. Gutierrez, L.J. Manso, P. Bustos and P. Núñez
Conference Papers Proc. of European Conference on Mobile Robots 2011, pp. 219--226. September 2011.

BibTeX

@inproceedings{bachiller2011incremental,
    author="P. Bachiller and M.A. Gutierrez and L.J. Manso and P. Bustos and P. Núñez",
    title="An incremental hybrid approach to indoor modeling",
    booktitle="Proc. of European Conference on Mobile Robots",
    year="2011",
    pages="219--226",
}

Abstract

A real-time synchronization algorithm between Text-To-Speech (TTS) system and Robot Mouth for Social Robotic Applications

F. Cid, R. Cintas, L.J. Manso, L. Calderita, A. Sánchez and P. Núñez
Conference Papers Proc. of Workshop of Physical Agents 2011, pp. 81--86. September 2011.

BibTeX

@inproceedings{cid2011realtime,
    author="F. Cid and R. Cintas and L.J. Manso and L. Calderita and A. Sánchez and P. Núñez",
    title="A real-time synchronization algorithm between Text-To-Speech (TTS) system and Robot Mouth for Social Robotic Applications",
    booktitle="Proc. of Workshop of Physical Agents",
    year="2011",
    pages="81--86",
}

Abstract

Attentional Behaviors for Environment Modeling by a Mobile Robot

Pilar Bachiller, Pablo Bustos and Luis J. Manso
Book Chapters Advances in Stereo Vision, InTech, ISBN 978-953-307-837-3, pp 17-40. July 2011.

BibTeX

@inbook{bachiller2011attentional,
    author="Pilar Bachiller and Pablo Bustos and Luis J. Manso",
    title="Attentional Behaviors for Environment Modeling by a Mobile Robot",
    pages="17--40",
    publisher="InTech",
    year="2011",
    type="bookchapter",
}

Abstract

Robust Behavior and Perception using Hierarchical State Machines: A Pallet Manipulation Experiment

Ramón Cintas, Luis J. Manso, Luis Pinero, Pilar Bachiller and Pablo Bustos
Journal Papers Journal of Physical Agents, ISSN 1888-0258. Vol. 5, No. 1, pp 35-44. March 2011.

BibTeX

@article{cintas2011robust,
    author="Ramón Cintas and Luis J. Manso and Luis Pinero and Pilar Bachiller and Pablo Bustos",
    title="Robust Behavior and Perception using Hierarchical State Machines: A Pallet Manipulation Experiment",
    journal="Journal of Physical Agents",
    year="2011",
    volume="5",
    number="1",
    pages="35-44",
}

Abstract

RoboComp: a Tool-based Robotics Framework

Luis J. Manso, Pilar Bachiller, Pablo Bustos, Pedro Núñez, Ramón Cintas and Luis Calderita
Book Chapters Simulation, Modeling and Programming for Autonomous Robots. Springer LNAI 6472, ISBN 978-3-540-89075-1, pp 251-262. November 2010.

BibTeX

@inbook{manso2010robocomp,
    author="Luis J. Manso and Pilar Bachiller and Pablo Bustos and Pedro Núñez and Ramón Cintas and Luis Calderita",
    title="RoboComp: a Tool-based Robotics Framework",
    pages="251--262",
    publisher="Springer",
    year="2010",
    type="bookchapter",
    isbn="978-3-540-89075-1",
}

Abstract

Improving a Robotics Framework with Real-Time and High-Performance Features

Jesús Martinez, Adrián Romero, Luis Manso and Pablo Bustos
Book Chapters Simulation, Modeling and Programming for Autonomous Robots. Springer LNAI 6472, ISBN 978-3-540-89075-1, pp 263-274. November 2010.

BibTeX

@inbook{martinez2010improving,
    author="Jesús Martinez and Adrián Romero and Luis Manso and Pablo Bustos",
    title="Improving a Robotics Framework with Real-Time and High-Performance Features",
    pages="263--274",
    publisher="Springer",
    year="2010",
    type="bookchapter",
    isbn="978-3-540-89075-1",
}

Abstract

RobEx: an Open-hardware Robotics Platform

José Mateos, Agustín Sánchez, Luis Manso, Pilar Bachiller and Pablo Bustos
Conference Papers Proc. of Workshop of Physical Agents, pp 17-24. ISBN 978-84-92812-54-7. September 2010.

BibTeX

@inproceedings{mateos2010robex,
    author="José Mateos and Agustín Sánchez and Luis Manso and Pilar Bachiller and Pablo Bustos",
    title="RobEx: an Open-hardware Robotics Platform",
    booktitle="Proc. of Workshop of Physical Agents",
    year="2010",
    pages="17--24",
}

Abstract

Visually-guided Object Manipulation by a Mobile Robot

Luis Pinero, Ramón Cintas, Luis Manso, Pilar Bachiller and Pablo Bustos
Conference Papers Proc. of Workshop of Physical Agents, pp 17-24. ISBN 978-84-92812-54-7. September 2010.

BibTeX

@inproceedings{pinero2010visuallyguided,
    author="Luis Pinero and Ramón Cintas and Luis Manso and Pilar Bachiller and Pablo Bustos",
    title="Visually-guided Object Manipulation by a Mobile Robot",
    booktitle="Proc. of Workshop of Physical Agents",
    year="2010",
    pages="145--152",
}

Abstract

Un Framework de Desarrollo para Robótica

Luis Manso, Pablo Bustos, Pilar Bachiller, Pedro Núñez, Ramón Cintas and Luis Calderita
Conference Papers Proceedings of I Jornadas Jóvenes Investigadores, pp 33-38, ISBN 978-84-693-1707-5. Cáceres, 17th April 2010.

BibTeX

@inproceedings{manso2010framework,
    author="Luis Manso and Pablo Bustos and Pilar Bachiller and Pedro Núñez and Ramón Cintas and Luis Calderita",
    title="Un Framework de Desarrollo para Robótica",
    booktitle="Proc. of I Jornadas Jóvenes Investigadores",
    year="2010",
    pages="33--38",
}

Abstract

Multi-cue Visual Obstacle Detection for Mobile Robots

Luis Manso, Pablo Bustos, Pilar Bachiller and José Moreno
Journal Papers Journal of Physical Agents, ISSN 1888-0258. Vol 4, No 1, pp 3-10. January 2010.

BibTeX

@article{manso2010multicue,
    author="Luis Manso and Pablo Bustos and Pilar Bachiller and José Moreno",
    title="Multi-cue Visual Obstacle Detection for Mobile Robots",
    journal="Journal of Physical Agents",
    year="2010",
    volume="4",
    number="1",
    pages="3-10",
}

Abstract

Obstacle Detection on Heterogeneous Surfaces Using Color and Geometric Cues

Luis Manso, Pablo Bustos, Pilar Bachiller and José Moreno
Conference Papers Proc. of Workshop of Physical Agents, pp 95-101, Cáceres, 10th-11th September 2009. ISBN 978-84-692-3220-0. 2009.

BibTeX

@inproceedings{manso2009obstacle,
    author="Luis Manso and Pablo Bustos and Pilar Bachiller and José Moreno",
    title="Obstacle Detection on Heterogeneous Surfaces Using Color and Geometric Cues",
    booktitle="Proc. of Workshop of Physical Agents",
    year="2009",
    pages="95--101",
}

Abstract

Navegación Visual en Robots Móviles

L.J. Manso
Theses M.Eng. thesis. Universidad de Extremadura.

BibTeX

@masterthesis{manso2009navegacion,
    author="L.J. Manso",
    title="Navegación Visual en Robots Móviles",
    school="Cáceres School of Technology, University of Extremadura",
    year="2009",
    type="masterthesis",
}

Abstract

Attentional Selection for Action in Mobile Robots

P. Bachiller, P. Bustos and L.J. Manso
Book Chapters Advances in Robotics, Automation and Control. I-Tech, pp 111-136, ISBN 78-953-7619-16-9. November 2008

BibTeX

@inbook{bachiller2008attentional,
    author="P. Bachiller and P. Bustos and L.J. Manso",
    title="Attentional Selection for Action in Mobile Robots",
    pages="111--136",
    publisher="I-Tech",
    year="2008",
    type="bookchapter",
}

Abstract