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
Chat with the Environment: Interactive Multimodal Perception Using Large Language Models
Xufeng Zhao, Mengdi Li, Cornelius Weber, Muhammad Burhan Hafez, Stefan Wermter
Understanding the Uncertainty Loop of Human-Robot Interaction
Jan Leusmann, Chao Wang, Michael Gienger, Albrecht Schmidt, Sven Mayer
Enable Natural Tactile Interaction for Robot Dog based on Large-format Distributed Flexible Pressure Sensors
Lishuang Zhan, Yancheng Cao, Qitai Chen, Haole Guo, Jiasi Gao, Yiyue Luo, Shihui Guo, Guyue Zhou, Jiangtao Gong