DFL-TORO：一种用于学习时间最优机器人制造任务的单次演示框架
DFL-TORO: A One-Shot Demonstration Framework for Learning Time-Optimal Robotic Manufacturing Tasks
AI总结：
本文提出 DFL-TORO 单次动觉演示框架，结合基于优化的时间最优、加加速度受限平滑算法，在 FR3 与 ABB YuMi 上验证了其提升制造机器人 LfD 效率和效果的能力。
摘要：
本文提出了 DFL-TORO，这是一种新颖的演示框架，通过单次动觉演示来学习时间最优机器人任务。它旨在优化应用于制造业的从演示中学习（LfD）流程。由于 LfD 的有效性受到人类演示质量和效率的挑战，我们的方法提供了一种简化方式，可直观地从人类教师处捕获任务需求，同时减少对多次演示的需求。此外，我们提出了一种基于优化的平滑算法，该算法可确保演示轨迹具有时间最优性和受调节的加加速度，同时遵守机器人的运动学约束。其结果是显著降低噪声，从而提高机器人的运行效率。使用 Franka Emika Research 3（FR3）机器人针对多种任务进行的评估进一步证实了本框架的有效性，并凸显了其在当代制造环境中变革动觉演示的潜力。此外，我们将所提出的框架引入由 ABB YuMi 机器人操作的真实制造环境，并通过动态运动基元（DMPs）开展案例研究，展示了其对 LfD 结果的积极影响。
英文摘要：
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. Moreover, we take our proposed framework into a real manufacturing setting operated by an ABB YuMi robot and showcase its positive impact on LfD outcomes by performing a case study via Dynamic Movement Primitives (DMPs).
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。
