Efficient DMP generalization to time-varying targets, external signals and via-points

Dynamic Movement Primitives (DMP) have found remarkable applicability and success in various robotic tasks, which can be mainly attributed to their generalization and robustness properties. Nevertheless, their generalization is based only on the trajectory endpoints (initial and target position). Moreover, the spatial generalization of DMP is known to suffer from shortcomings like over-scaling and mirroring of the motion. In this work we propose a novel generalization scheme, based on optimizing online the DMP weights so that the acceleration profile and hence the underlying training trajectory pattern is preserved. This approach remedies the shortcomings of the classical DMP scaling and additionally allows the DMP to generalize also to intermediate points (via-points) and external signals (coupling terms), while preserving the training trajectory pattern. Extensive comparative simulations with the classical and other DMP variants are conducted, while experimental results validate the applicability and efficacy of the proposed method.