  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.
