NIPS 2013: Probabilistic Movement Primitives

Movement Primitives (MP) are a well-established approach for representing modular and re-usable robot movement generators. Many state-of-the-art robot learning successes are based MPs, due to their compact representation of the inherently continuous and high dimensional robot movements. A major goal in robot learning is to combine multiple MPs as building blocks in a modular control architecture to solve complex tasks. To this effect, a MP representation has to allow for blending between motions, adapting to altered task variables, and co-activating multiple MPs in parallel. We present a probabilistic formulation of the MP concept that maintains a distribution over trajectories. Our probabilistic approach allows for the derivation of new operations which are essential for implementing all aforementioned properties in one framework. In order to use such a trajectory distribution for robot movement control, we analytically derive a stochastic feedback controller which reproduces the given trajectory distribution. We evaluate and compare our approach to existing methods on several simulated as well as real robot scenarios.

  • A. Paraschos, C. Daniel, J. Peters, and G. Neumann, “Probabilistic movement primitives,” in Advances in Neural Information Processing Systems, (NIPS), 2013.
    [BibTeX] [Abstract] [Download PDF]

    Movement Primitives (MP) are a well-established approach for representing modular and re-usable robot movement generators. Many state-of-the-art robot learning successes are based MPs, due to their compact representation of the inherently continuous and high dimensional robot movements. A major goal in robot learning is to combine multiple MPs as building blocks in a modular control architecture to solve complex tasks. To this effect, a MP representation has to allow for blending between motions, adapting to altered task variables, and co-activating multiple MPs in parallel. We present a probabilistic formulation of the MP concept that maintains a distribution over trajectories. Our probabilistic approach allows for the derivation of new operations which are essential for implementing all aforementioned properties in one framework. In order to use such a trajectory distribution for robot movement control, we analytically derive a stochastic feedback controller which reproduces the given trajectory distribution. We evaluate and compare our approach to existing methods on several simulated as well as real robot scenarios.

    @inproceedings{lirolem25785,
    booktitle = {Advances in Neural Information Processing Systems, (NIPS)},
    year = {2013},
    journal = {Advances in Neural Information Processing Systems},
    author = {A. Paraschos and C. Daniel and J. Peters and G. Neumann},
    title = {Probabilistic movement primitives},
    month = {December},
    abstract = {Movement Primitives (MP) are a well-established approach for representing modular
    and re-usable robot movement generators. Many state-of-the-art robot learning
    successes are based MPs, due to their compact representation of the inherently
    continuous and high dimensional robot movements. A major goal in robot learning
    is to combine multiple MPs as building blocks in a modular control architecture
    to solve complex tasks. To this effect, a MP representation has to allow for
    blending between motions, adapting to altered task variables, and co-activating
    multiple MPs in parallel. We present a probabilistic formulation of the MP concept
    that maintains a distribution over trajectories. Our probabilistic approach
    allows for the derivation of new operations which are essential for implementing
    all aforementioned properties in one framework. In order to use such a trajectory
    distribution for robot movement control, we analytically derive a stochastic feedback
    controller which reproduces the given trajectory distribution. We evaluate
    and compare our approach to existing methods on several simulated as well as
    real robot scenarios.},
    url = {http://eprints.lincoln.ac.uk/25785/},
    keywords = {ARRAY(0x56147f3751e8)}
    }