This paper presents DFL-TORO, a novel Demonstration Framework for Learning Time-Optimal Robotic tasks via One-shot kinesthetic demonstration. It aims at optimizing the process of Learning from Demonstration (LfD), applied in the manufacturing sector. As the effectiveness of LfD is challenged by the quality and efficiency of human demonstrations, our approach offers a streamlined method to intuitively capture task requirements from human teachers, by reducing the need for multiple demonstrations. Furthermore, we propose an optimization-based smoothing algorithm that ensures time-optimal and jerk-regulated demonstration trajectories, while also adhering to the robot's kinematic constraints. The result is a significant reduction in noise, thereby boosting the robot's operation efficiency. Evaluations using a Franka Emika Research 3 (FR3) robot for a reaching task further substantiate the efficacy of our framework, highlighting its potential to transform kinesthetic demonstrations in contemporary manufacturing environments.

翻译：本文提出DFL-TORO，一种创新的通过单次动觉示教学习节时最优机器人任务的示教框架。该框架旨在优化制造业中应用的基于示教学习（LfD）过程。针对人类示教质量和效率对LfD有效性的制约，我们的方法提供了一种简洁的途径，通过减少对多次示教的需求，直观地捕捉人类教师的任务要求。此外，我们提出了一种基于优化的平滑算法，确保示教轨迹在时间上最优且加加速度受控，同时满足机器人的运动学约束。该算法显著降低了噪声，从而提升了机器人的操作效率。使用Franka Emika Research 3（FR3）机器人进行到达任务的评估进一步证实了我们框架的有效性，突显了其改变现代制造业中动觉示教的潜力。