A virtual reality framework for fast dataset creation applied to cloth manipulation with automatic semantic labelling

© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Teaching complex manipulation skills, such as folding garments, to a bi-manual robot is a very challenging task, which is often tackled through learning from demonstration. The few datasets of garment-folding demonstrations available nowadays to the robotics research community have been either gathered from human demonstrations or generated through simulation. The former have the great difficulty of perceiving both cloth state and human action as well as transferring them to the dynamic control of the robot, while the latter require coding human motion into the simulator in open loop, i.e., without incorporating the visual feedback naturally used by people, resulting in far-from-realistic movements. In this article, we present an accurate dataset of human cloth folding demonstrations. The dataset is collected through our novel virtual reality (VR) framework, based on Unity’s 3D platform and the use of an HTC Vive Pro system. The framework is capable of simulating realistic garments while allowing users to interact with them in real time through handheld controllers. By doing so, and thanks to the immersive experience, our framework permits exploiting human visual feedback in the demonstrations while at the same time getting rid of the difficulties of capturing the state of cloth, thus simplifying data acquisition and resulting in more realistic demonstrations. We create and make public a dataset of cloth manipulation sequences, whose cloth states are semantically labeled in an automatic way by using a novel low-dimensional cloth representation that yields a very good separation between different cloth configurations.The research leading to these results receives funding from the European Research Council (ERC) from the European Union Horizon 2020 Programme under grant agreement no. 741930 (CLOTHILDE: CLOTH manIpulation Learning from DEmonstrations) and project SoftEnable (HORIZONCL4-2021-DIGITAL-EMERGING-01-101070600). Authors also received funding from project CHLOE-GRAPH (PID2020-118649RB-I00) funded by MCIN/ AEI /10.13039/501100011033 and COHERENT (PCI2020-120718-2) funded by MCIN/ AEI /10.13039/501100011033 and cofunded by the ”European Union NextGenerationEU/PRTR”.Peer ReviewedPostprint (author's final draft

Speech Development by Imitation

The Double Cone Model (DCM) is a model of how the brain transforms sensory input to motor commands through successive stages of data compression and expansion. We have tested a subset of the DCM on speech recognition, production and imitation. The experiments show that the DCM is a good candidate for an artificial speech processing system that can develop autonomously. We show that the DCM can learn a repertoire of speech sounds by listening to speech input. It is also able to link the individual elements of speech to sequences that can be recognized or reproduced, thus allowing the system to imitate spoken language

INTRODUCCIóN A LA ROBóTICA

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Designing Robots for Care: Care Centered Value-Sensitive Design

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The shift towards human-robot collaboration (HRC) has the potential to increase productivity and sustainability, while reducing costs for the manufacturing industries. Indeed, it holds great potential for workplaces, allowing individuals to forsake repetitive or physically demanding jobs to focus on safer and more fulfilling ones. Still, integration of humans and machines in organizations presents great challenges to IS scholars due to the complexity of aligning digitalization and human resources. A knowledge gap does persist about organizational implications when it comes to implement collaborative robotics in the workplace and to support proper HRC. Thus, this paper aims to identify recommended human resources management (HRM) practices from previous research about human-robot interaction (HRI). As our results highlight that few studies attempted to fill the gap, a conceptual framework is proposed. It integrates HRM practices, technology adoption dimensions and main determinants of HRC, in the objective to support collaborative robotics implementation in organizations