Scalable, Fast, Highly-Accurate Human-to-Robot Skill Transfer for the Dexterous, Efficient Operation of Histology Microtomes

Humans are capable of performing intricate and complex tasks, enabling seamless interaction with their surroundings. Therefore, capturing the human demonstrated skills and transferring these skills to robots is beneficial when engaging with and executing tasks in a human-oriented world. However, these demonstrations are not always transferable as kinematic differences can prevent robots from properly replicating the human demonstrated strategies. In this work, we propose a human-to-robot skill transfer system, where the human demonstrator wears and directly controls the robot’s end-effectors with appropriate interfaces. These skills are then transferred in a scalable, fast, and highly-accurate manner to the robotic system for the operation of a pathology microtome. The goal is to address hospital labour gaps by introducing highly-dexterous robotic automation.