Movement primitives (MPs) are compact representations of robot skills that can be learned from demonstrations and combined into complex behaviors. However, merely equipping robots with a fixed set of innate MPs is insufficient to deploy them in dynamic and unpredictable environments. Instead, the full potential of MPs remains to be attained via adaptable, large-scale MP libraries. In this paper, we propose a set of seven fundamental operations to incrementally learn, improve, and re-organize MP libraries. To showcase their applicability, we provide explicit formulations of the spatial operations for libraries composed of Via-Point Movement Primitives (VMPs). By building on Riemannian manifold theory, our approach enables the incremental learning of all parameters of position and orientation VMPs within a library. Moreover, our approach stores a fixed number of parameters, thus complying with the essential principles of incremental learning. We evaluate our approach to incrementally learn a VMP library from motion capture data provided sequentially.

翻译：运动原语（MPs）是机器人技能的紧凑表示形式，可通过示教学习并组合成复杂行为。然而，仅赋予机器人固定的一组先天运动原语不足以在动态不可预测环境中部署。相反，运动原语的完整潜力需通过可自适应的大规模运动原语库来实现。本文提出七种基本操作，用于增量学习、改进与重组运动原语库。为展示其适用性，我们针对基于路径点运动原语（VMPs）构成的库给出了空间操作的显式公式。通过建立在黎曼流形理论基础上，本方法能够增量学习库中位置与方向VMPs的所有参数。此外，本方法存储固定数量的参数，因此符合增量学习的基本原则。我们通过顺序提供的运动捕捉数据评估了增量学习VMP库的方法。