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
Towards Safe Multi-Level Human-Robot Interaction in Industrial Tasks
Zhe Huang, Ye-Ji Mun, Haonan Chen, Yiqing Xie, Yilong Niu, Xiang Li, Ninghan Zhong, Haoyuan You, D. Livingston McPherson, Katherine Driggs-Campbell
Towards socially-competent and culturally-adaptive artificial agents Expressive order, interactional disruptions and recovery strategies
Chiara Bassetti, Enrico Blanzieri, Stefano Borgo, Sofia Marangon
Customizing Textile and Tactile Skins for Interactive Industrial Robots
Bo Ying Su, Zhongqi Wei, James McCann, Wenzhen Yuan, Changliu Liu