Humanoid Grasping with Multi-Finger Dexterous Hands Based on DM-KMP

Humanoid grasping plays a crucial role in robotic manipulation. Currently, most humanoid grasping methods exhibit limitations in control and generalization when handling complex and diverse grasping tasks. In this study, we proposed a novel humanoid grasping control method based on dynamic movement primitives (DMP) and kernelized movement primitives (KMP), namely DM-KMP. We conducted an experiment involving the grasping of 12 different objects using a multi-finger dexterous hand. The results showed that the DM-KMP method achieved a success rate of 90.8%, significantly higher than the 77.5% success rate of the traditional KMP method and the 81.7% success rate of the DMP method. These findings indicated that the proposed DM-KMP method could not only improve the grasping performance for multi-finger robotic hands but also show flexibility of modulating the reference trajectories, making it more suitable for complex and varied grasping tasks. This novel method may improve the performance of grasping with multi-finger dexterous hand and can be widely applied in a variety of application such as industrial or in-house manipulative tasks.