Dynamic Movement Primitives (DMP) have found remarkable applicability and success in various robotic tasks, which can be mainly attributed to their generalization, modulation and robustness properties. Nevertheless, the spatial generalization of DMP can be problematic in some cases, leading to excessive or unnatural spatial scaling. Moreover, incorporating intermediate points (via-points) to adjust the DMP trajectory, is not adequately addressed. In this work we propose an improved online spatial generalization, that remedies the shortcomings of the classical DMP generalization, and moreover allows the incorporation of dynamic via-points. This is achieved by designing an online adaptation scheme for the DMP weights which is proved to minimize the distance from the demonstrated acceleration profile in order to retain the shape of the demonstration, subject to dynamic via-point and initial/final state constraints. Extensive comparative simulations with the classical and other DMP variants are conducted, while experimental results validate the applicability and efficacy of the proposed method.

翻译：动态运动基元（DMP）已在多种机器人任务中展现出显著的应用价值与成功，这主要归功于其泛化、调制和鲁棒性特性。然而，在某些情况下DMP的空间泛化可能存在问题，导致过度或不自然的空间缩放。此外，融入用于调整DMP轨迹的中间点（过路点）尚未得到充分解决。本文提出了一种改进的在线空间泛化方法，不仅弥补了经典DMP泛化的缺陷，还允许融入动态过路点。通过设计一种DMP权重的在线自适应方案，该方案被证明能在满足动态过路点及初始/最终状态约束的条件下，最小化与演示加速度轨迹的距离，从而保留演示的形状。基于经典及其他DMP变体的大量对比仿真实验已完成，同时实验结果验证了所提方法的适用性与有效性。