Dual-arm robot manipulation is applicable to many domains, such as industrial, medical, and home service scenes. Learning from demonstrations is a highly effective paradigm for robotic learning, where a robot learns from human actions directly and can be used autonomously for new tasks, avoiding the complicated analytical calculation for motion programming. However, the learned skills are not easy to generalize to new cases where special constraints such as varying relative distance limitation of robotic end effectors for human-like cooperative manipulations exist. In this paper, we propose a dynamic movement primitives (DMPs) based skills learning framework for redundant dual-arm robots. The method, with a coupling acceleration term to the DMPs function, is inspired by the transient performance control of Barrier Lyapunov Functions. The additional coupling acceleration term is calculated based on the constant joint distance and varying relative distance limitations of end effectors for object-approaching actions. In addition, we integrate the generated actions in joint space and the solution for a redundant dual-arm robot to complete a human-like manipulation. Simulations undertaken in Matlab and Gazebo environments certify the effectiveness of the proposed method.

双臂机器人操控适用于工业、医疗、家庭服务等多个领域。从演示中学习是机器人学习的一种高效范式，机器人直接从人类动作中学习，可以自主用于新任务，避免了复杂的运动编程分析计算。然而，学习到的技能并不容易推广到新的案例，在这些案例中，存在特殊约束，例如机器人末端执行器的不同相对距离限制，用于类人协作操作。在本文中，我们为冗余双臂机器人提出了一种基于动态运动原语（DMP）的技能学习框架。该方法具有与 DMPs 函数的耦合加速项，其灵感来自 Barrier Lyapunov 函数的瞬态性能控制。附加耦合加速度项是基于恒定的关节距离和物体接近动作的末端执行器的变化相对距离限制来计算的。此外，我们集成了关节空间中生成的动作和冗余双臂机器人的解决方案，以完成类人操作。在 Matlab 和 Gazebo 环境中进行的模拟证明了所提出方法的有效性。