面向激光护肤程序的几何约束学习节律轨迹
Learning Rhythmic Trajectories with Geometric Constraints for Laser-Based Skincare Procedures
AI总结：
本文提出一种基于结构化预测的模仿学习算法，使机器人能够学习并适应激光护肤中的节律轨迹，处理几何约束，并在真实实验中成功模仿皮肤科手术。该算法有效解决了准周期运动的模仿挑战，并适应新场景。
摘要：
机器人的日益部署显著提升了众多行业的自动化水平。本文研究了美容行业中基于激光的皮肤科手术的自动化挑战；这一组相关的操作任务涉及将美容激光的能量以重复模式传递到皮肤上。为了自动化这一程序，我们提出使用机器人操作器，并通过从演示中学习的框架赋予其熟练皮肤科医生的灵巧性。为了确保美容激光能够正确地将能量传递到个体的皮肤表面，我们开发了一种新颖的基于结构化预测的模仿学习算法，其优点在于处理几何约束。值得注意的是，我们提出的算法有效解决了与准周期运动相关的模仿挑战，这是许多基于激光的美容任务的常见特征。所进行的真实世界实验展示了我们的机器人美容师在模仿真实皮肤科手术方面的性能；我们的新方法不仅能够复制所提供的演示中的节律运动，还能将所学的技能适应于先前未见过的场景和对象。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
