Human Robot Collaboration
Human-robot collaboration (HRC) focuses on designing systems where humans and robots work together efficiently and safely to achieve shared goals. Current research emphasizes improving communication and understanding between humans and robots, often employing large language models (LLMs), deep learning models for perception (e.g., computer vision, human pose estimation), and advanced planning algorithms (e.g., hierarchical task networks, Bayesian optimization) to enable more natural and adaptable interactions. This field is crucial for advancing automation in various sectors, from manufacturing and construction to healthcare and domestic settings, by creating more efficient, safer, and user-friendly collaborative workspaces.
Papers
CoNav: A Benchmark for Human-Centered Collaborative Navigation
Changhao Li, Xinyu Sun, Peihao Chen, Jugang Fan, Zixu Wang, Yanxia Liu, Jinhui Zhu, Chuang Gan, Mingkui Tan
Enhancing Human-Robot Collaborative Assembly in Manufacturing Systems Using Large Language Models
Jonghan Lim, Sujani Patel, Alex Evans, John Pimley, Yifei Li, Ilya Kovalenko
Anticipate & Collab: Data-driven Task Anticipation and Knowledge-driven Planning for Human-robot Collaboration
Shivam Singh, Karthik Swaminathan, Raghav Arora, Ramandeep Singh, Ahana Datta, Dipanjan Das, Snehasis Banerjee, Mohan Sridharan, Madhava Krishna
Integrating Large Language Models with Multimodal Virtual Reality Interfaces to Support Collaborative Human-Robot Construction Work
Somin Park, Carol C. Menassa, Vineet R. Kamat