The dynamic motion primitive-based (DMP) method is an effective method of learning from demonstrations. However, most of the current DMP-based methods focus on learning one task with one module. Although, some deep learning-based frameworks can learn to multi-task at the same time. However, those methods require a large number of training data and have limited generalization of the learned behavior to the untrained state. In this paper, we propose a framework that combines the advantages of the traditional DMP-based method and conditional variational auto-encoder (CVAE). The encoder and decoder are made of a dynamic system and deep neural network. Deep neural networks are used to generate torque conditioned on the task ID. Then, this torque is used to create the desired trajectory in the dynamic system based on the final state. In this way, the generated tractory can adjust to the new goal position. We also propose a finetune method to guarantee the via-point constraint. Our model is trained on the handwriting number dataset and can be used to solve robotic tasks -- reaching and pushing directly. The proposed model is validated in the simulation environment. The results show that after training on the handwriting number dataset, it achieves a 100\% success rate on pushing and reaching tasks.

翻译：基于动态运动基元的方法是一种有效的示教学习方法。然而，当前大多数基于动态运动基元的方法侧重于使用单一模块学习单一任务。尽管一些基于深度学习的框架能够同时学习多任务，但这些方法需要大量训练数据，且所学行为对未训练状态的泛化能力有限。本文提出一种结合传统基于动态运动基元方法与条件变分自编码器优势的框架。编码器和解码器由动态系统与深度神经网络构成。深度神经网络用于根据任务ID生成条件扭矩，该扭矩随后在动态系统中基于最终状态生成期望轨迹。通过这种方式，生成的轨迹能够适应新的目标位置。我们还提出一种微调方法来保证路径点约束。模型在手写数字数据集上进行训练，并可直接用于解决机器人任务——到达与推动。所提模型在仿真环境中得到验证，结果表明：在手写数字数据集训练后，模型在推动与到达任务上实现了100%的成功率。