A novel trajectory learning method for robotic arms based on Gaussian Mixture Model and k-value selection algorithm.

ABSTRACT: In the field of robotic arm trajectory imitation learning, Gaussian Mixture Models are widely used for their ability to capture the characteristics of complex trajectories. However, one major challenge in utilizing these models lies in the initialization process, particularly in determining the number of Gaussian kernels, or the k-value. The choice of the k-value significantly impacts the model's performance, and traditional methods, such as random selection or selection based on empirical knowledge, often lead to suboptimal outcomes. To address this challenge, this paper proposes a novel trajectory learning method for robotic arms that combines Gaussian Mixture Model with a k-value selection algorithm. The proposed approach leverages the principles of the elbow method along with the prope

SUBMITTER: Yan J 

PROVIDER: S-EPMC11828358 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature