Publikation
movement_primitives: Imitation Learning of Cartesian Motion with Movement Primitives
Alexander Fabisch
In: Journal of Open Source Software (JOSS), Vol. 9, No. 97, Pages 1-9, The Open Journal, 3/2024.
Zusammenfassung
Movement primitives are a common representation of movements in robotics (Maeda et al.,
2017) for imitation learning, reinforcement learning, and black-box optimization of behaviors.
There are many types and variations. The Python library movement_primitives focuses on
imitation learning (see Figure 1), generalization, and adaptation of movement primitives
in Cartesian space. It implements dynamical movement primitives, probabilistic movement
primitives, as well as Cartesian and dual Cartesian movement primitives with coupling terms to
constrain relative movements in bimanual manipulation. They are implemented in Cython to
speed up online execution and batch processing in an offline setting. In addition, the library
provides tools for data analysis and movement evaluation.