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 Robust Filter for Marker-less Multi-person Tracking in Human-Robot Interaction Scenarios
Enrico Martini, Harshil Parekh, Shaoting Peng, Nicola Bombieri, Nadia Figueroa
Evaluating MEDIRL: A Replication and Ablation Study of Maximum Entropy Deep Inverse Reinforcement Learning for Human Social Navigation
Vinay Gupta, Nihal Gunukula
Real-Time Dynamic Robot-Assisted Hand-Object Interaction via Motion Primitives
Mingqi Yuan, Huijiang Wang, Kai-Fung Chu, Fumiya Iida, Bo Li, Wenjun Zeng
Uniform vs. Lognormal Kinematics in Robots: Perceptual Preferences for Robotic Movements
Jose J. Quintana, Miguel A. Ferrer, Moises Diaz, Jose J. Feo, Adam Wolniakowski, Konstantsin Miatliuk
Value Alignment and Trust in Human-Robot Interaction: Insights from Simulation and User Study
Shreyas Bhat, Joseph B. Lyons, Cong Shi, X. Jessie Yang
An Open-Source Reproducible Chess Robot for Human-Robot Interaction Research
Renchi Zhang, Joost de Winter, Dimitra Dodou, Harleigh Seyffert, Yke Bauke Eisma
Safety Control of Service Robots with LLMs and Embodied Knowledge Graphs
Yong Qi, Gabriel Kyebambo, Siyuan Xie, Wei Shen, Shenghui Wang, Bitao Xie, Bin He, Zhipeng Wang, Shuo Jiang