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Dynamic Movement Primitives (DMP) have found remarkable applicability andsuccess in various robotic tasks, which can be mainly attributed to theirgeneralization, modulation and robustness properties. Nevertheless, the spatialgeneralization of DMP can be problematic in some cases, leading to excessive orunnatural spatial scaling. Moreover, incorporating intermediate points(via-points) to adjust the DMP trajectory, is not adequately addressed. In thiswork we propose an improved online spatial generalization, that remedies theshortcomings of the classical DMP generalization, and moreover allows theincorporation of dynamic via-points. This is achieved by designing an onlineadaptation scheme for the DMP weights which is proved to minimize the distancefrom the demonstrated acceleration profile to retain the shape of thedemonstration, subject to dynamic via-point and initial/final stateconstraints. Extensive comparative simulations with the classical and other DMPvariants are conducted, while experimental results validate the applicabilityand efficacy of the proposed method.

Dynamic via-points and improved spatial generalization for online trajectory planning with Dynamic Movement Primitives