  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.
