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）凭借其泛化性、调节性和鲁棒性，在各类机器人任务中展现出显著的应用价值与成功表现。然而，DMP的空间泛化在某些情况下存在问题，容易导致过度或不自然的空间缩放。此外，如何将中间点（途经点）融入DMP轨迹调整的问题尚未得到充分解决。本文提出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，还实现了动态途经点的融合。该方法通过设计DMP权重的在线自适应机制，在满足动态途经点约束及初始/最终状态约束的前提下，最小化与示教加速度轮廓的距离，从而保留示教轨迹的形状。通过与经典DMP及其他变体的大量对比仿真，以及实验结果验证，证明了所提方法的适用性和有效性。