Dynamic Movement Primitives Based Robot Skills Learning

In this article, a robot skills learning framework is developed, which considers both motion modeling and execution. In order to enable the robot to learn skills from demonstrations, a learning method called dynamic movement primitives (DMPs) is introduced to model motion. A staged teaching strategy is integrated into DMPs frameworks to enhance the generality such that the complicated tasks can be also performed for multi-joint manipulators. The DMP connection method is used to make an accurate and smooth transition in position and velocity space to connect complex motion sequences. In addition, motions are categorized into different goals and durations. It is worth mentioning that an adaptive neural networks (NNs) control method is proposed to achieve highly accurate trajectory tracking and to ensure the performance of action execution, which is beneficial to the improvement of reliability of the skills learning system. The experiment test on the Baxter robot verifies the effectiveness of the proposed method.

Automated summary

In this article, a robot skills learning framework is developed, integrating motion modeling and execution using dynamic movement primitives (DMPs). The framework includes a staged teaching strategy and DMP connection method to handle complex tasks for multi-joint manipulators. Additionally, motions are categorized based on goals and durations. An adaptive neural networks (NNs) control method is proposed to ensure accurate trajectory tracking and reliable performance of action execution. Experimental tests on the Baxter robot confirm the effectiveness of the proposed method.