Learning Rhythmic Trajectories with Geometric Constraints for Laser-Based Skincare Procedures

TL;DR

This paper presents a novel imitation learning algorithm for robotic dermatology procedures that handles geometric constraints and quasi-periodic motions, enabling robots to perform precise laser-based skincare tasks by learning from demonstrations.

Contribution

It introduces a structured prediction-based imitation learning method specifically designed for rhythmic, geometric-constrained motions in laser dermatology, improving robotic skill transfer.

Findings

Successfully mimics dermatological laser procedures

Adapts to new scenarios and subjects

Handles quasi-periodic motion challenges

Abstract

The increasing deployment of robots has significantly enhanced the automation levels across a wide and diverse range of industries. This paper investigates the automation challenges of laser-based dermatology procedures in the beauty industry; This group of related manipulation tasks involves delivering energy from a cosmetic laser onto the skin with repetitive patterns. To automate this procedure, we propose to use a robotic manipulator and endow it with the dexterity of a skilled dermatology practitioner through a learning-from-demonstration framework. To ensure that the cosmetic laser can properly deliver the energy onto the skin surface of an individual, we develop a novel structured prediction-based imitation learning algorithm with the merit of handling geometric constraints. Notably, our proposed algorithm effectively tackles the imitation challenges associated with quasi-periodic motions, a common feature of many laser-based cosmetic tasks. The conducted real-world experiments illustrate the performance of our robotic beautician in mimicking realistic dermatological procedures; Our new method is shown to not only replicate the rhythmic movements from the provided demonstrations but also to adapt the acquired skills to previously unseen scenarios and subjects.