Human–Robot Skill Transferring and Inverse Velocity Admittance Control for Soft Tissue Cutting Tasks

Abstract

Robotic meat cutting is increasingly in demand in meat industries due to safety issues, labor shortages, and inefficiencies. This paper proposes a multi-demonstration human–robot skill transfer framework to address the flexible and generalized cutting of sheep hindquarters with complex 3D anatomy structures by imitating humans. To improve the generalization with meat sizes and demonstrations and extract target cutting behaviors, multi-demonstrations of cutting are encoded into low-dimension latent space through principal components analysis (PCA), Gaussian mixture model (GMM), and Gaussian mixture regression (GMR). To improve the robotic cutting flexibility and the cutting behavior reproducing accuracy, this study combines a modified dynamic movement primitive (DMP) high-level behavior generator with the low-level joints admittance control (AC) through real-time inverse velocity (IV) kinematics solving and constructs the IVAC-DMP control module. The experimental results show that the maximum residual meat thickness in the sheep hindquarter cutting of sample 1 is 3.1 mm, and sample 2 is 3.8 mm. The residual rates of samples 1 and 2 are 5.6% and 4.8%. Both meet the requirements for sheep hindquarter separation. The proposed framework is advantageous for harvesting high-value meat products and providing a reference technique for robot skill learning in interaction tasks.

Keywords

• sheep hindquarters
• robotic meat cutting
• learning from demonstration
• admittance control
• Gaussian mixture regression