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Embodied learning is a theoretical framework for educators that is based on different fields, theories, and models that support the embodied mind in education. Based on the literature, the framework presents three hypotheses: 1) perceptual inference is at the core of education; 2) ‘surprise’ is critical in learning; 3) pedagogies can be divided into maximizing evidence from observations and prior knowledge. The paper presents work in human-computer interaction and teaching practice to test and exemplify how the framework can capitalize on the simplicity of predictive processing to exploit meaning from sounds, smells, and textures.

SUMMARYIn human–robot comanipulation, virtual guides are an important tool used to assist the human worker as they constrain the movement of the robot to improve the task accuracy and to avoid undesirable effects, such as collisions with the environment. Consequently, the physical effort and cognitive overload are reduced during accomplishment of comanipulative tasks. However, the construction of virtual guides often requires expert knowledge and modeling of the task, which restricts the usefulness of virtual guides to scenarios with fixed constraints. Moreover, few approaches have addressed the implementation of virtual guides enforcing orientation constraints and, when done, these approaches have treated translation and orientation separately, and consequently there is no synchronization of the translational and rotational motions. To overcome these challenges and enhance the programming flexibility of virtual guides, we present a new framework that allows the user to create 6D virtual guides through XSplines which we define as a combination of Akima splines for the translation component and spherical cubic interpolation of quaternions for the orientation component. For complex tasks, the user is able to initially define a 3D virtual guide and then use this assistance in translational motion to concentrate only on defining the orientations along the path. It is also possible for the user to modify a particular point or portion of a guide while being assisted by it. We demonstrate in an industrial scenario that these innovations provide an intuitive solution to extend the use of virtual guides to 6 degrees of freedom and increase the human worker’s comfort during the programming phase of these guides in an assisted human–robot comanipulation context.

Global Navigation Satellite Systems (GNSS) systems are largely affected by ionospheric effects, which are larger in low latitudes. Traditional mitigation approaches rely either on high cost equipment or simplified models that have shown limitations for the complex ionospheric dynamics of the equatorial region. Data availability from increasingly larger GNSS receiver networks, together with scalable machine learning techniques, provides the foundation to approach this problem from a data driven perspective. This paper shows the usage of data driven models to predict the Vertical Total Electron Content (VTEC) of the ionosphere based on pseudoranges measurements and, thus, improve GNSS positioning using single frequency receivers especially in low latitudes. Our results reduce by more than 60% the error in VTEC prediction as compared to the Klobuchar model used by GPS, achieving an average positioning residual error below 1 meter, both in medium and low latitudes and in periods of high and low solar activity.

An efficient exploitation of the Distributed Computing Infrastructures (DCIs) is specially needed to deal with the data deluge that the scientific community, in particular the Astrophysics one, is facing. This requires a good understanding of the underlying DCIs. Science Gateways (SGs) provide the users with an environment that eases the interaction with the DCIs. As a previous step, IT skilled users should populate the SGs with friendly but advanced tools (e.g. Workflows, visualization tools) that not only support the scientists to build their own experiments but also adapt them in an optimal way to the infrastructures. In Astronomy, the Virtual Observatory provides the community with services and tools for data access and sharing. However, state of the art telescopes and the coming Square Kilometre Array (SKA), able to reach data rates in the exa-scale domain, will also require advanced tools for data analysis and visualization that should be run on DCIs as well as shared on SGs. In the here presented work, we have selected as exemplar a set of analysis tasks of interest for some SKA use cases. These analysis tasks have been implemented as web services that use the COMPSs programming model in order to achieve a more efficient use of the DCIs. At the same time, the nature of the web services turns them into blocks that the astronomers can combine with VO services to build their own workflows. The web services and the workflows built upon them form a two-level workflow system that hides the technical details of the DCIs and exploits them efficiently. This approach is used for the first time in analytical tasks of interest for the SKA that benefits from the capabilities of the DCIs.

We present the concept of bio-feedback driven computing to design a responsive narrative, which acts according to the readers experience. We explore on how to detect engagement and give our evaluation on the usefulness of different sensor modalities. We find temperature and blink frequency are best to estimate engagement and can classify engaging and non-engaging user-independent without error for a small user sample size (5 users).

We present an initial prototype to extend a reading tablet for haptic feedback -with a surface vibration transducer. This, in order to transform literature into a multimodal reading experience. In this augmented literature, the text provides its reader with verbal information, while other senses perceive context through non-verbal cues. We use our own framework to design this storytelling across senses integrating text, sound, animation and tactile sensation. The intention is to develop a multimodal storytelling that enhances the reader's imagination, improving the reading experience. In this way, we believe, readers will completely immerse into the narrative and enjoy reading more.

International audience; Virtual guiding fixtures constrain the movements of a robot to task-relevant trajectories, and have been successfully applied to, for instance, surgical and manufacturing tasks. Whereas previous work has considered guiding fixtures for single tasks, in this paper we propose a library of guiding fixtures for multiple tasks, and propose methods for 1) Creating and adding guides based on machine learning; 2) Selecting guides on-line based on probabilistic implementation of guiding fixtures; 3) Refining existing guides based on an incremental learning method. We demonstrate in an industrial task that a library of guiding fixtures provides an intuitive haptic interface for joint human-robot completion of tasks, and improves performance in terms of task execution time, mental workload and errors.