Human Robot Interaction
Human-robot interaction (HRI) research focuses on designing robots that can effectively and naturally interact with humans, aiming to improve collaboration, communication, and overall user experience. Current research emphasizes developing robots capable of understanding and responding to diverse human behaviors, including speech, gestures, and even physiological signals, often employing machine learning models like vision transformers, convolutional neural networks, and reinforcement learning algorithms to achieve this. These advancements are significant because they pave the way for safer, more intuitive, and productive human-robot collaborations across various domains, from industrial settings to assistive technologies and service robotics.
Papers
A Study in Zucker: Insights on Interactions Between Humans and Small Service Robots
Alex Day, Ioannis Karamouzas
ArUcoGlide: a Novel Wearable Robot for Position Tracking and Haptic Feedback to Increase Safety During Human-Robot Interaction
Ali Alabbas, Miguel Altamirano Cabrera, Oussama Alyounes, Dzmitry Tsetserukou
Towards Language-Based Modulation of Assistive Robots through Multimodal Models
Philipp Wicke, Lüfti Kerem Şenel, Shengqiang Zhang, Luis Figueredo, Abdeldjallil Naceri, Sami Haddadin, Hinrich Schütze
Happily Error After: Framework Development and User Study for Correcting Robot Perception Errors in Virtual Reality
Maciej K. Wozniak, Rebecca Stower, Patric Jensfelt, Andre Pereira