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
FlowAct: A Proactive Multimodal Human-robot Interaction System with Continuous Flow of Perception and Modular Action Sub-systems
Timothée Dhaussy, Bassam Jabaian, Fabrice Lefèvre
Benchmarking ML Approaches to UWB-Based Range-Only Posture Recognition for Human Robot-Interaction
Salma Salimi, Sahar Salimpour, Jorge Peña Queralta, Wallace Moreira Bessa, Tomi Westerlund
Toward human-centered shared autonomy AI paradigms for human-robot teaming in healthcare
Reza Abiri, Ali Rabiee, Sima Ghafoori, Anna Cetera
Reacting on human stubbornness in human-machine trajectory planning
Julian Schneider, Niels Straky, Simon Meyer, Balint Varga, Sören Hohmann
Long-Term, Store-Front Robotics: Interactive Music for Robotic Arm, Caxixi and Frame Drums
Richard Savery, Fouad Sukkar