Robot skill learning is one of the international advanced directions in the field of robot-based intelligent manufacturing, which makes it possible for robots to learn and operate autonomously in complex real-world environments. In this paper, we propose a multitasking-oriented robot skill learning framework named MT-RSL to improve the efficiency and robustness of multi-task robot skill learning in complex real-world environments, and present the detailed design steps of three key sub-modules included in MT-RSL, namely, sub-task segmentation module, robot skill learning module, and robot configuration optimization module. Firstly, we design a novel sub-task segmentation module based on a coarse-to-fine sub-task segmentation (CF-STS) strategy, in which the Finite State Machine (FSM) is used to analyze complex robot behaviors to obtain a coarse robot sub-task sequence, and the Beta Process Autoregressive Hidden Markov Model (BP-AR-HMM) is used to establish the connection and dependence between multiple demonstration trajectories and encode these trajectories, so as to obtain a finer robot action sequence. Secondly, we extend the basic DMPs system to a continuous dynamic movement primitives (CDMPs) system to construct a novel robot skill learning module, which improves the efficiency of the robot to learn skills and perform actions by orderly coordinating sub-parts such as the activation signals, motion actuator, DMPs-based learning module, and robot configuration optimization module. Then, we design a novel robot configuration optimization module, which introduces the velocity directional manipulability measure (VDM) as the evaluation index of robot kinematic performance to establish the robot configuration optimization model, and proposes an improved probabilistic adaptive particle swarm optimization (Pro-APSO) algorithm to solve this optimization model, so as to obtain the optimal robot configuration. Finally, we develop an experimental testing platform based on the Robot Operating System (ROS) and conduct a series of prototype experiments in complex real-world scenarios. The experimental results demonstrate that our proposed MT-RSL can significantly improve the effectiveness and robustness of multi-task robot skill learning, and can outperform existing robot skill learning methods in terms of both learning efficiency, VDM, and success rate.

中文翻译：

---

MT-RSL：基于连续动态运动原语的面向多任务的机器人技能学习框架，用于提高机器人智能操作的效率和质量


机器人技能学习是基于机器人的智能制造领域的国际先进方向之一，它使得机器人能够在复杂的现实环境中自主学习和操作。在本文中，我们提出了一种名为 MT-RSL 的面向多任务的机器人技能学习框架，以提高复杂现实环境中多任务机器人技能学习的效率和鲁棒性，并介绍了三个关键子模块的详细设计步骤MT-RSL中包含子任务分割模块、机器人技能学习模块、机器人配置优化模块。首先，我们设计了一种基于从粗到细子任务分割（CF-STS）策略的新型子任务分割模块，其中有限状态机（FSM）用于分析复杂的机器人行为以获得粗略的机器人行为。机器人子任务序列，利用Beta过程自回归隐马尔可夫模型（BP-AR-HMM）建立多个演示轨迹之间的联系和依赖，并对这些轨迹进行编码，从而获得更精细的机器人动作序列。其次，我们将基本的DMPs系统扩展到连续动态运动原语（CDMPs）系统，构建了一种新颖的机器人技能学习模块，通过有序协调激活等子部分来提高机器人学习技能和执行动作的效率信号、运动执行器、基于 DMP 的学习模块和机器人配置优化模块。 然后，设计了一种新颖的机器人构形优化模块，引入速度方向可操作性测量（VDM）作为机器人运动学性能的评价指标，建立机器人构形优化模型，并提出一种改进的概率自适应粒子群优化（Pro-APSO） )算法来求解这个优化模型，从而获得最优的机器人配置。最后，我们开发了基于机器人操作系统（ROS）的实验测试平台，并在复杂的现实场景中进行了一系列原型实验。实验结果表明，我们提出的MT-RSL可以显着提高多任务机器人技能学习的有效性和鲁棒性，并且在学习效率、VDM和成功率方面都优于现有的机器人技能学习方法。