一种统一的多层框架用于从不完美的人类示范中学习技能
A Unified Multi-Layer Framework for Skill Acquisition from Imperfect Human Demonstrations
AI总结：
本文提出了一种多层控制框架，通过实时学习轨迹和变量阻抗提升效率，结合奇异点优化和基础合规性方法，实现安全、直观的机器人协作任务系统。
摘要：
当前的人机交互（HRI）系统在技能教学中碎片化，现有方法缺乏同时高效、直观和安全的统一框架。本文提出了一种新型分层控制框架，通过基于通用机器人合规性的鲁棒学习从示范中学习，分为三个递进阶段：首先，实时学习轨迹和变量阻抗；其次，通过空域优化策略管理奇异点并提供一致交互感；最后，引入基础空域合规方法，使机器人在不牺牲主要任务性能的情况下适应外部交互。实验验证了该框架在7自由度KUKA LWR机器人上的有效性，展示了更安全、直观和高效的统一系统。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
