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
HiCRISP: An LLM-based Hierarchical Closed-Loop Robotic Intelligent Self-Correction Planner
Chenlin Ming, Jiacheng Lin, Pangkit Fong, Han Wang, Xiaoming Duan, Jianping He
Exploring Human's Gender Perception and Bias toward Non-Humanoid Robots
Mahya Ramezani, Jose Luis Sanchez-Lopez
Person Re-Identification for Robot Person Following with Online Continual Learning
Hanjing Ye, Jieting Zhao, Yu Zhan, Weinan Chen, Li He, Hong Zhang
Sequential annotations for naturally-occurring HRI: first insights
Lucien Tisserand, Frédéric Armetta, Heike Baldauf-Quilliatre, Antoine Bouquin, Salima Hassas, Mathieu Lefort
CEFHRI: A Communication Efficient Federated Learning Framework for Recognizing Industrial Human-Robot Interaction
Umar Khalid, Hasan Iqbal, Saeed Vahidian, Jing Hua, Chen Chen