A vision-based humanoid compliant skill transfer framework: Application to robotic cutting tasks

Autonomously completing a contact-rich task for multiple manipulation objects remains a challenging problem for robots. To achieve this goal, learning from demonstration has emerged as an efficient method for transferring human-like skills to robots. Existing works primarily focus on trajectory or impedance learning to design force-impedance controllers for specific tasks, which require precise force sensing. However, visual perception plays a critical role in enabling humans to perform dexterous manipulation. To bridge the gap between vision and learning in the control loop, this work proposes a vision-based humanoid compliant skill transfer (VHCST) framework. Considering the lack of vision-impedance mapping, a hybrid tree is introduced as a planning bridge to encode skill parameters across multiple objects. To simplify skill transfer, an observation-wearable demonstration method is employed to capture the position and stiffness of human’s arm. The decoupled learning model incorporates the geometric properties of stiffness ellipsoids, which reside on Riemannian manifolds. Finally, the proposed approach is validated through robotic cutting experiments involving multiple objects. Comparative experimental results demonstrate the effectiveness of the proposed framework.