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
A Service Robot in the Wild: Analysis of Users Intentions, Robot Behaviors, and Their Impact on the Interaction
Simone Arreghini, Gabriele Abbate, Alessandro Giusti, Antonio Paolillo
Analysis and Detection of Differences in Spoken User Behaviors between Autonomous and Wizard-of-Oz Systems
Mikey Elmers, Koji Inoue, Divesh Lala, Keiko Ochi, Tatsuya Kawahara
An Approach to Elicit Human-Understandable Robot Expressions to Support Human-Robot Interaction
Jan Leusmann, Steeven Villa, Thomas Liang, Chao Wang, Albrecht Schmidt, Sven Mayer
Human-Robot Collaborative Minimum Time Search through Sub-priors in Ant Colony Optimization
Oscar Gil Viyuela, Alberto Sanfeliu
Data Augmentation for 3DMM-based Arousal-Valence Prediction for HRI
Christian Arzate Cruz, Yotam Sechayk, Takeo Igarashi, Randy Gomez
QUB-PHEO: A Visual-Based Dyadic Multi-View Dataset for Intention Inference in Collaborative Assembly
Samuel Adebayo, Seán McLoone, Joost C. Dessing
Built Different: Tactile Perception to Overcome Cross-Embodiment Capability Differences in Collaborative Manipulation
William van den Bogert, Madhavan Iyengar, Nima Fazeli
A Multimedia Framework for Continuum Robots: Systematic, Computational, and Control Perspectives
Po-Yu Hsieh, June-Hao Hou
SiSCo: Signal Synthesis for Effective Human-Robot Communication Via Large Language Models
Shubham Sonawani, Fabian Weigend, Heni Ben Amor
Personalization in Human-Robot Interaction through Preference-based Action Representation Learning
Ruiqi Wang, Dezhong Zhao, Dayoon Suh, Ziqin Yuan, Guohua Chen, Byung-Cheol Min