New AURO Paper: Using Probabilistic Movement Primitives in Robotics

Well deserved, Alex!

  • A. Paraschos, C. Daniel, J. Peters, and G. Neumann, “Using probabilistic movement primitives in robotics,” Autonomous Robots, vol. 42, iss. 3, pp. 529-551, 2018.
    [BibTeX] [Abstract] [Download PDF]

    Movement Primitives are a well-established paradigm for modular movement representation and generation. They provide a data-driven representation of movements and support generalization to novel situations, temporal modulation, sequencing of primitives and controllers for executing the primitive on physical systems. However, while many MP frameworks exhibit some of these properties, there is a need for a uni- fied framework that implements all of them in a principled way. In this paper, we show that this goal can be achieved by using a probabilistic representation. Our approach models trajectory distributions learned from stochastic movements. Probabilistic operations, such as conditioning can be used to achieve generalization to novel situations or to combine and blend movements in a principled way. We derive a stochastic feedback controller that reproduces the encoded variability of the movement and the coupling of the degrees of freedom of the robot. We evaluate and compare our approach on several simulated and real robot scenarios.

    @article{lirolem27883,
    author = {Alexandros Paraschos and Christian Daniel and Jan Peters and Gerhard Neumann},
    pages = {529--551},
    journal = {Autonomous Robots},
    title = {Using probabilistic movement primitives in robotics},
    month = {March},
    year = {2018},
    publisher = {Springer Verlag},
    number = {3},
    volume = {42},
    keywords = {ARRAY(0x564e3c684750)},
    abstract = {Movement Primitives are a well-established
    paradigm for modular movement representation and
    generation. They provide a data-driven representation
    of movements and support generalization to novel situations,
    temporal modulation, sequencing of primitives
    and controllers for executing the primitive on physical
    systems. However, while many MP frameworks exhibit
    some of these properties, there is a need for a uni-
    fied framework that implements all of them in a principled
    way. In this paper, we show that this goal can be
    achieved by using a probabilistic representation. Our
    approach models trajectory distributions learned from
    stochastic movements. Probabilistic operations, such as
    conditioning can be used to achieve generalization to
    novel situations or to combine and blend movements in
    a principled way. We derive a stochastic feedback controller
    that reproduces the encoded variability of the
    movement and the coupling of the degrees of freedom
    of the robot. We evaluate and compare our approach
    on several simulated and real robot scenarios.},
    url = {http://eprints.lincoln.ac.uk/27883/}
    }