This paper presents a comprehensive framework to enhance Human-Robot Collaboration (HRC) in real-world scenarios. It introduces a formalism to model articulated tasks, requiring cooperation between two agents, through a smaller set of primitives. Our implementation leverages Hierarchical Task Networks (HTN) planning and a modular multisensory perception pipeline, which includes vision, human activity recognition, and tactile sensing. To showcase the system's scalability, we present an experimental scenario where two humans alternate in collaborating with a Baxter robot to assemble four pieces of furniture with variable components. This integration highlights promising advancements in HRC, suggesting a scalable approach for complex, cooperative tasks across diverse applications.
@article{arxiv.2406.04907,
title = {A Modular Framework for Flexible Planning in Human-Robot Collaboration},
author = {Valerio Belcamino and Mariya Kilina and Linda Lastrico and Alessandro Carfì and Fulvio Mastrogiovanni},
journal= {arXiv preprint arXiv:2406.04907},
year = {2024}
}