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 variety of tasks further substantiate the efficacy of our framework, highlighting its potential to transform kinesthetic demonstrations in contemporary manufacturing environments.

翻译：本文提出了DFL-TORO，一种通过一次性动觉示教实现时间最优机器人任务学习的新型示教框架。该框架旨在优化应用于制造业中的"从示教中学习"（LfD）过程。针对人类示教的质量与效率对LfD有效性的制约，本方法通过减少多次示教需求，提供了一种从人类教师直观获取任务要求的精简方案。此外，我们提出了一种基于优化的平滑算法，在满足机器人运动学约束的前提下，确保生成时间最优且无急冲的示教轨迹。该算法显著降低了轨迹噪声，从而提升了机器人的操作效率。通过Franka Emika Research 3（FR3）机器人对多种任务的评估，进一步验证了本框架的有效性，突显了其在现代制造环境中革新动觉示教方式的潜力。