"Pieters, R." - Selaus tekijän mukaan TUNICRIS-julkaisut

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    • Automatic Robot Path Planning for Visual Inspection from Object Shape 

      Tasneem, O.; Pieters, R.
      IEEE International Conference on Automation Science and Engineering (2024)
      conference
      Visual inspection is a crucial yet time-consuming task across various industries. Numerous established methods employ machine learning in inspection tasks, necessitating specific training data that includes predefined ...

    • Co-speech gestures for human-robot collaboration 

      Ekrekli, A.; Angleraud, A.; Sharma, G.; Pieters, R. (30.11.2023)
      conference
      Collaboration between human and robot requires effective modes of communication to assign robot tasks and coordinate activities. As communication can utilize different modalities, a multi-modal approach can be more expressive ...

    • Many Faced Robot - Design and Manufacturing of a Parametric, Modular and Open Source Robot Head 

      Netzev, M.; Houbre, Q.; Airaksinen, E.; Angleraud, A.; Pieters, R. (2019)
      conference
      Robots developed for social interaction and care show great promise as a tool to assist people. While the functionality and capability of such robots is crucial in their acceptance, the visual appearance should not be ...

    • Multi-label Annotation for Visual Multi-Task Learning Models 

      Sharma, G.; Angleraud, A.; Pieters, R. (30.11.2023)
      conference
      Deep learning requires large amounts of data, and a well-defined pipeline for labeling and augmentation. Current solutions support numerous computer vision tasks with dedicated annotation types and formats, such as bounding ...

    • OpenDR: An Open Toolkit for Enabling High Performance, Low Footprint Deep Learning for Robotics 

      Passalis, N.; Pedrazzi, S.; Babuska, R.; Burgard, W.; Dias, D.; Ferro, F.; Gabbouj, M.; Green, O.; Iosifidis, A.; Kayacan, E.; Kober, J.; Michel, O.; Nikolaidis, N.; Nousi, P.; Pieters, R.; Tzelepi, M.; Valada, A.; Tefas, A. (2022)
      conference
      Existing Deep Learning (DL) frameworks typically do not provide ready-to-use solutions for robotics, where very specific learning, reasoning, and embodiment problems exist. Their relatively steep learning curve and the ...