Feelix: A Haptic Design Tool for Rigid and Soft Actuation Systems with Machine Learning Support скачать в хорошем качестве

Feelix: A Haptic Design Tool for Rigid and Soft Actuation Systems with Machine Learning Support

by Anke van Oosterhout, Serhat Demirtaş, Jamie Paik, and Miguel Bruns Reconfigurable Robotics Lab École Polytechnique Fédérale de Lausanne (EPFL) http://rrl.epfl.ch Industrial Design Eindhoven University of Technology (TU/e) Feelix: A Haptic Design Tool for Rigid and Soft Actuation Systems with Machine Learning Support Abstract — This paper showcases the latest advancements of Feelix, a haptic design tool for rigid and soft actuation systems with multiple degrees of freedom. The new features of Feelix introduce support for designing feedback for pneumatic actuation systems, in addition to the existing support for brushless motor control. With its intuitive graphical editor, users can quickly design effects that can be immediately experienced on the hardware device, enabling fast exploration to support the design process. The sensors embedded in the hardware device provide real-time feedback to the design tool, which can be utilized to train machine learning models through Feelix's new pattern recognition feature. Our presentation will demonstrate Feelix's capabilities for rigid and soft actuation devices with multiple degrees of freedom to showcase the tool's versatile applications in designing haptic experiences. We would like to thank Zhenishbek Zhakypov for his contribution to the design of the soft robotic haptic interface that has been employed to implement the pneumatic design feature in Feelix. This work has been demonstrated at the IEEE World Haptics 2023 conference in Delft, Netherlands. #WorldHaptics2023