  We propose a novel framework for enhancing robotic adaptability and learning
efficiency, which integrates unsupervised trajectory segmentation with adaptive
probabilistic movement primitives (ProMPs). By employing a cutting-edge deep
learning architecture that combines autoencoders and Recurrent Neural Networks
(RNNs), our approach autonomously pinpoints critical transitional points in
continuous, unlabeled motion data, thus significantly reducing dependence on
extensively labeled datasets. This innovative method dynamically adjusts motion
trajectories using conditional variables, significantly enhancing the
flexibility and accuracy of robotic actions under dynamic conditions while also
reducing the computational overhead associated with traditional robotic
programming methods. Our experimental validation demonstrates superior learning
efficiency and adaptability compared to existing techniques, paving the way for
advanced applications in industrial and service robotics.
