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.

翻译：本文提出一个综合性框架，旨在提升真实场景中的人机协作（HRC）能力。该框架通过一组精简的基元，构建了一种形式化模型来描述需两个智能体协同完成的组合式任务。我们的实现采用了分层任务网络（HTN）规划与模块化多模态感知管线，该管线融合了视觉感知、人体活动识别与触觉传感技术。为展示系统的可扩展性，我们设计了一个实验场景：两名人类操作员交替与Baxter机器人协作，组装四件包含可变组件的家具。该集成方案展现了人机协作领域的前沿进展，为跨多种应用的复杂协作任务提供了可扩展的解决路径。