SUMMARYDynamic movement primitives (DMP) are motion building blocks suitable for real-world tasks. We suggest a methodology for learning the manifold of task and DMP parameters, which facilitates runtime adaptation to changes in task requirements while ensuring predictable and robust performance. For efficient learning, the parameter space is analyzed using principal component analysis and locally linear embedding. Two manifold learning methods: kernel estimation and deep neural networks, are investigated for a ball throwing task in simulation and in a physical environment. Low runtime estimation errors are obtained for both learning methods, with an advantage to kernel estimation when data sets are small.

中文翻译：

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基于学习任务流形和动态运动原始参数的运动适应

摘要动态运动原语 (DMP) 是适用于现实世界任务的运动构建块。我们建议了一种用于学习各种任务和 DMP 参数的方法，这有助于运行时适应任务要求的变化，同时确保可预测和稳健的性能。为了高效学习，使用主成分分析和局部线性嵌入来分析参数空间。针对模拟和物理环境中的投球任务研究了两种流形学习方法：核估计和深度神经网络。两种学习方法都获得了较低的运行时估计误差，当数据集较小时，对内核估计具有优势。