Current Human-Robot Interaction (HRI) systems for skill teaching are fragmented, and existing approaches in the literature do not offer a cohesive framework that is simultaneously efficient, intuitive, and universally safe. This paper presents a novel, layered control framework that addresses this fundamental gap by enabling robust, compliant Learning from Demonstration (LfD) built upon a foundation of universal robot compliance. The proposed approach is structured in three progressive and interconnected stages. First, we introduce a real-time LfD method that learns both the trajectory and variable impedance from a single demonstration, significantly improving efficiency and reproduction fidelity. To ensure high-quality and intuitive {kinesthetic teaching}, we then present a null-space optimization strategy that proactively manages singularities and provides a consistent interaction feel during human demonstration. Finally, to ensure generalized safety, we introduce a foundational null-space compliance method that enables the entire robot body to compliantly adapt to post-learning external interactions without compromising main task performance. This final contribution transforms the system into a versatile HRI platform, moving beyond end-effector (EE)-specific applications. We validate the complete framework through comprehensive comparative experiments on a 7-DOF KUKA LWR robot. The results demonstrate a safer, more intuitive, and more efficient unified system for a wide range of human-robot collaborative tasks.

翻译：当前用于技能教授的人机交互系统是碎片化的，文献中现有方法未能提供一种同时具备高效性、直觉性和普遍安全性的统一框架。本文提出了一种新颖的分层控制框架，通过以通用机器人柔顺性为基础构建鲁棒且柔顺的学习从示教方法，弥补了这一根本性缺口。所提方法被构建为三个渐进且相互关联的阶段。首先，我们引入了一种实时LfD方法，该方法能从单次示教中同时学习轨迹与可变阻抗，显著提升了效率与复现保真度。为确保高质量且直觉性的动觉示教，我们接着提出了一种零空间优化策略，该策略能主动管理奇异点并在人类示教过程中提供一致交互感受。最后，为确保广义安全性，我们引入了一种基础的零空间柔顺方法，能使机器人本体整体在不损害主任务性能的前提下，柔顺适应学习后的外部交互。这一最终贡献将系统转化为一个多功能人机交互平台，超越了仅针对末端执行器的应用。我们通过在7自由度KUKA LWR机器人上的全面对比实验验证了该完整框架。结果表明，该统一系统在广泛的人机协作任务中展现出更安全、更直觉且更高效的特点。