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
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
ARMCHAIR: integrated inverse reinforcement learning and model predictive control for human-robot collaboration
Angelo Caregnato-Neto, Luciano Cavalcante Siebert, Arkady Zgonnikov, Marcos Ricardo Omena de Albuquerque Maximo, Rubens Junqueira Magalhães Afonso
On the Design of Human-Robot Collaboration Gestures
Anas Shrinah, Masoud S. Bahraini, Fahad Khan, Seemal Asif, Niels Lohse, Kerstin Eder