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
Situated Participatory Design: A Method for In Situ Design of Robotic Interaction with Older Adults
Laura Stegner, Emmanuel Senft, Bilge Mutlu
Upper-limb Geometric MyoPassivity Map for Physical Human-Robot Interaction
Xingyuan Zhou, Peter Paik, S. Farokh Atashzar
Shutter, the Robot Photographer: Leveraging Behavior Trees for Public, In-the-Wild Human-Robot Interactions
Alexander Lew, Sydney Thompson, Nathan Tsoi, Marynel Vázquez