A Unified Multi-Layer Framework for Skill Acquisition from Imperfect Human Demonstrations

2026-04-09

AI Summary

Existing human-robot interactive skill teaching systems often suffer from fragmentation, struggling to simultaneously ensure efficiency, intuitiveness, and global safety. This work proposes a three-tier hierarchical control framework that, for the first time, jointly learns trajectory and variable impedance from a single demonstration. By integrating active null-space singularity management with whole-body compliant control, the approach transcends the limitations of conventional end-effector-centric methods and enables safe, full-body collaborative interaction. Validated on a KUKA LWR robot, the method significantly enhances skill reproduction fidelity, teaching intuitiveness, and overall system safety, demonstrating broad applicability across diverse human-robot cooperative tasks.

Abstract

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.

Problem

• Human-Robot Interaction
• Learning from Demonstration
• Skill Acquisition
• Robot Compliance
• Imperfect Demonstrations

Innovation

• Learning from Demonstration
• Variable Impedance Control
• Null-Space Optimization
• Whole-Body Compliance
• Human-Robot Interaction