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This paper presents DFL-TORO, a novel Demonstration Framework for LearningTime-Optimal Robotic tasks via One-shot kinesthetic demonstration. It aims atoptimizing the process of Learning from Demonstration (LfD), applied in themanufacturing sector. As the effectiveness of LfD is challenged by the qualityand efficiency of human demonstrations, our approach offers a streamlinedmethod to intuitively capture task requirements from human teachers, byreducing the need for multiple demonstrations. Furthermore, we propose anoptimization-based smoothing algorithm that ensures time-optimal andjerk-regulated demonstration trajectories, while also adhering to the robot'skinematic constraints. The result is a significant reduction in noise, therebyboosting the robot's operation efficiency. Evaluations using a Franka EmikaResearch 3 (FR3) robot for a reaching task further substantiate the efficacy ofour framework, highlighting its potential to transform kinestheticdemonstrations in contemporary manufacturing environments.

DFL-TORO: A One-Shot Demonstration Framework for Learning Time-Optimal Robotic Manufacturing Tasks