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.

翻译：我们提出了一种增强机器人适应性与学习效率的新型框架，该框架将无监督轨迹分割与自适应概率运动基元（ProMPs）相结合。通过采用融合自编码器与循环神经网络（RNNs）的前沿深度学习架构，我们的方法能够自主定位连续未标注运动数据中的关键过渡点，从而显著减少对大规模标注数据集的依赖。这种创新方法利用条件变量动态调整运动轨迹，在动态条件下大幅提升机器人动作的灵活性与精确度，同时降低传统机器人编程方法的计算开销。实验验证表明，与现有技术相比，该方法在学习效率与适应性方面具有显著优势，为工业及服务机器人的高级应用开辟了新路径。