Feature and performance comparison of the V-REP, Gazebo and ARGoS robot simulators

© Springer International Publishing AG, part of Springer Nature 2018. In this paper, the characteristics and performance of three open-source simulators for robotics, V-REP, Gazebo and ARGoS, are thoroughly analysed and compared. While they all allow for programming in C++, they also represent clear alternatives when it comes to the trade-off between complexity and performance. Attention is given to their built-in features, robot libraries, programming methods and the usability of their user interfaces. Benchmark test results are reported in order to identify how well the simulators can cope with environments of varying complexity. The richness of features of V-REP and the strong performance of Gazebo and ARGoS in complex scenes are highlighted. Various usability issues of Gazebo are also noted

Pharmacological effect of one icv dose of Allopregnanolone in female rat: behavioural profile

We have previously observed that intracerebroventricular allopregnanolone (ALLO) injection produced an anxiolytic effect and inhibited sexual receptivity when the test was performed in a separate manner. Also, ALLO reverts learning deficit in female rats in the hippocampi. To study the behavioral effects of an acute treatment with ALLO in the right lateral ventricle we used two approaches: a- A battery test to analyze the anxiety and mating behavior. And b- The avoidance test and novel object recognition test to evaluate its effect on memory and learning. Ovariectomized rats were injected with estrogen and progesterone. After it ALLO or vehicle were administered into the right lateral ventricle. To reach the objective (a) rats were put in a sequential battery test in the next order: 1-Open field. 2- Plus maze task. 3- Mating behavior. For the aim (b) it was performed a Novel Object Recognition Test and Step-down Inhibitory Avoidance Task. ALLO did not affect locomotors-exploratory behavior. Animals treated with ALLO, spent more time and had more entries into the open arm in a plus maze task and lordosis quotient was lower than in the control group. ALLO increased the latency in step down test and had no effects on discrimination index test in NORT. Here we demonstrated that one pharmacological dose of ALLO in ovariectomized primed rats is enough to generate all changes observed in the battery test. Moreover, the acute treatment with ALLO in lateral ventricle enhanced the memory acquisition in an avoidance task.Fil: Pelegrina, Laura Tatiana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto de Medicina y Biología Experimental de Cuyo; ArgentinaFil: Escudero, Carla Gimena. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto de Medicina y Biología Experimental de Cuyo; ArgentinaFil: Giuliani, Fernando Alfredo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto de Medicina y Biología Experimental de Cuyo; ArgentinaFil: García Menéndez, Sebastián Marcelo Manuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto de Medicina y Biología Experimental de Cuyo; ArgentinaFil: Cabrera Kreiker, Ricardo Jorge. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto de Medicina y Biología Experimental de Cuyo; ArgentinaFil: Laconi, Myriam Raquel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto de Medicina y Biología Experimental de Cuyo; Argentin

Impact of resolution, colour, and motion on object identification in digital twins from robot sensor data

This paper makes a contribution to research on digital twins that are generated from robot sensor data. We present the results of an online user study in which 240 participants were tasked to identify real-world objects from robot point cloud data. In the study we manipulated the render style (point clouds vs voxels), render resolution (i.e., density of point clouds and granularity of voxel grids), colour (monochrome vs coloured points/voxels), and motion (no motion vs rotational motion) of the shown objects to measure the impact of these attributes on object recognition performance. A statistical analysis of the study results suggests that there is a three-way interaction between our independent variables. Further analysis suggests: 1) objects are easier to recognise when rendered as point clouds than when rendered as voxels, particularly lower resolution voxels; 2) the effect of colour and motion is affected by how objects are rendered, e.g., utility of colour decreases with resolution for point clouds; 3) an increased resolution of point clouds only leads to an increased object recognition if points are coloured and static; 4) high resolution voxels outperform medium and low resolution voxels in all conditions, but there is little difference between medium and low resolution voxels; 5) motion is unable to improve the performance of voxels at low and medium resolutions, but is able to improve performance for medium and low resolution point clouds. Our results have implications for the design of robot sensor suites and data gathering and transmission protocols when creating digital twins from robot gathered point cloud data

Systematic analysis of video data from different human-robot interaction studies: A categorisation of social signals during error situations

Human–robot interactions are often affected by error situations that are caused by either the robot or the human. Therefore, robots would profit from the ability to recognize when error situations occur. We investigated the verbal and non-verbal social signals that humans show when error situations occur in human–robot interaction experiments. For that, we analyzed 201 videos of five human–robot interaction user studies with varying tasks from four independent projects. The analysis shows that there are two types of error situations: social norm violations and technical failures. Social norm violations are situations in which the robot does not adhere to the underlying social script of the interaction. Technical failures are caused by technical shortcomings of the robot. The results of the video analysis show that the study participants use many head movements and very few gestures, but they often smile, when in an error situation with the robot. Another result is that the participants sometimes stop moving at the beginning of error situations. We also found that the participants talked more in the case of social norm violations and less during technical failures. Finally, the participants use fewer non-verbal social signals (for example smiling, nodding, and head shaking), when they are interacting with the robot alone and no experimenter or other human is present. The results suggest that participants do not see the robot as a social interaction partner with comparable communication skills. Our findings have implications for builders and evaluators of human–robot interaction systems. The builders need to consider including modules for recognition and classification of head movements to the robot input channels. The evaluators need to make sure that the presence of an experimenter does not skew the results of their user studies

Alkaline magmas in shallow arc plutonic roots: a field and experimental investigation of hydrous cumulate melting in the southern Adamello batholith

Despite the first-order importance of crystallisation–differentiation for arc magma evolution, several other processes contribute to their compositional diversity. Among them is the remelting of partly crystallised magmas, also known as cumulate melting or ‘petrological cannibalism’. The impact of this process on the plutonic record is poorly constrained. We investigate a nepheline-normative dyke suite close to the Blumone gabbros, a large amphibole-gabbro unit of the Tertiary Southern Alpine Adamello igneous complex. The compositions of the studied dykes are characterised by low SiO2 (43–46 wt. %), MgO (5.0–7.2 wt. %), Ni (18–40 μg/g), and high Al2O3 (20.2–22.0 wt. %) contents. Phenocrystic plagioclase in these dykes exhibits major, trace, and Sr isotope compositions similar to Blumone cumulate plagioclase, suggesting a genetic link between the nepheline-normative dykes and the amphibole-gabbro cumulates. We tested this hypothesis by performing saturation experiments on a nepheline-normative dyke composition in an externally heated pressure vessel at 200 MPa between 975 and 1100 °C at fO2 conditions close to the Ni–NiO buffer. Plagioclase and spinel are near-liquidus phases at and above 1050 °C, contrasting with the typical near-liquidus olivine ± spinel assemblage in hydrous calc-alkaline basalts. The alkaline nature of the dykes results from the abundance of amphibole in the protolith, consistent with melting of amphibole-gabbro cumulates. We modelled the heat budget from the repeated injection of basaltic andesite into a partly crystallised amphibole-gabbro cumulate. The results of this model show that no more than 7% of the cumulate pile reaches temperatures high enough to produce nepheline-normative melts. We propose that such nepheline-normative dykes are a hallmark of hydrous cumulate melting in subvolcanic plumbing systems. Therefore, ne-normative dykes in arc batholiths may indicate periods with high magma fluxes

Using Ellipsis Detection and Word Similarity for Transformation of Spoken Language into Grammatically Valid Sentences

When humans speak they often use gram-matically incorrect sentences, which is a problem for grammar-based language pro-cessing methods, since they expect in-put that is valid for the grammar. We present two methods to transform spoken language into grammatically correct sen-tences. The first is an algorithm for au-tomatic ellipsis detection, which finds el-lipses in spoken sentences and searches in a combinatory categorial grammar for suitable words to fill the ellipses. The sec-ond method is an algorithm that computes the semantic similarity of two words us-ing WordNet, which we use to find alter-natives to words that are unknown to the grammar. In an evaluation, we show that the usage of these two methods leads to an increase of 38.64 % more parseable sen-tences on a test set of spoken sentences that were collected during a human-robot interaction experiment.

User-centred design and evaluation of a tele-operated echocardiography robot

We present the collected findings of a user-centred approach for developing a tele-operated robot for remote echocardiography examinations. During the three-year development of the robot, we involved users in all development stages of the robot, to increase the usability of the system for the doctors. For requirement compilation, we conducted a literature review, observed two traditional examinations, arranged focus groups with doctors and patients, and conducted two online surveys. During the development of the robot, we regularly involved doctors in usability tests to receive feedback from them on the user interface for the robot and on the robot’s hardware. For evaluation of the robot, we conducted two eye tracking studies. In the first study, doctors executed a traditional echocardiography examination. In the second study, the doctors conducted a remote examination with our robot. The results of the studies show that all doctors were able to successfully complete a correct ultrasonography examination with the tele-operated robot. In comparison to a traditional examination, the doctors on average only need a short amount of additional time to successfully examine a patient when using our remote echocardiography robot. The results also show that the doctors fixate considerably more often, but with shorter fixation times, on the USG screen in the traditional examination compared to the remote examination. We found further that some of the user-centred design methods we applied had to be adjusted to the clinical context and the hectic schedule of the doctors. Overall, our experience and results suggest that the usage of user-centred design methodology is well suited for developing medical robots and leads to a usable product that meets the end users’ needs

Automatically Classifying User Engagement for Dynamic Multi-party Human–Robot Interaction

© 2017, The Author(s). A robot agent designed to engage in real-world human–robot joint action must be able to understand the social states of the human users it interacts with in order to behave appropriately. In particular, in a dynamic public space, a crucial task for the robot is to determine the needs and intentions of all of the people in the scene, so that it only interacts with people who intend to interact with it. We address the task of estimating the engagement state of customers for a robot bartender based on the data from audiovisual sensors. We begin with an offline experiment using hidden Markov models, confirming that the sensor data contains the information necessary to estimate user state. We then present two strategies for online state estimation: a rule-based classifier based on observed human behaviour in real bars, and a set of supervised classifiers trained on a labelled corpus. These strategies are compared in offline cross-validation, in an online user study, and through validation against a separate test corpus. These studies show that while the trained classifiers are best in a cross-validation setting, the rule-based classifier performs best with novel data; however, all classifiers also change their estimate too frequently for practical use. To address this issue, we present a final classifier based on Conditional Random Fields: this model has comparable performance on the test data, with increased stability. In summary, though, the rule-based classifier shows competitive performance with the trained classifiers, suggesting that for this task, such a simple model could actually be a preferred option, providing useful online performance while avoiding the implementation and data-scarcity issues involved in using machine learning for this task

Combining goal inference and natural-language dialogue for human-robot joint action

We demonstrate how combining the reasoning components from two existing systems designed for human-robot joint action produces an integrated system with greater capabilities than either of the individual systems. One of the systems supports primarily non-verbal interaction and uses dynamic neural fields to infer the user’s goals and to suggest appropriate system responses; the other emphasises natural-language interaction and uses a dialogue manager to process user input and select appropriate system responses. Combining these two methods of reasoning results in a robot that is able to coordinate its actions with those of the user while employing a wide range of verbal and non-verbal communicative actions.(undefined