IJJR 2017: Learning Movement Primitive Libraries through Probabilistic Segmentation

Movement primitives are a well established approach for encoding and executing movements. While the primitives themselves have been extensively researched, the concept of movement primitive libraries has not received similar attention. Libraries of movement primitives represent the skill set of an agent. Primitives can be queried and sequenced in order to solve specific tasks. The goal of this work is to segment unlabeled demonstrations into a representative set of primitives. Our proposed method differs from current approaches by taking advantage of the often neglected, mutual dependencies between the segments contained in the demonstrations and the primitives to be encoded. By exploiting this mutual dependency, we show that we can improve both the segmentation and the movement primitive library. Based on probabilistic inference our novel approach segments the demonstrations while learning a probabilistic representation of movement primitives. We demonstrate our method on two real robot applications. First, the robot segments sequences of different letters into a library, explaining the observed trajectories. Second, the robot segments demonstrations of a chair assembly task into a movement primitive library. The library is subsequently used to assemble the chair in an order not present in the demonstrations.

  • R. Lioutikov, G. Neumann, G. Maeda, and J. Peters, “Learning movement primitive libraries through probabilistic segmentation,” International Journal of Robotics Research (IJRR), vol. 36, iss. 8, pp. 879-894, 2017.
    [BibTeX] [Abstract] [Download PDF]

    Movement primitives are a well established approach for encoding and executing movements. While the primitives themselves have been extensively researched, the concept of movement primitive libraries has not received similar attention. Libraries of movement primitives represent the skill set of an agent. Primitives can be queried and sequenced in order to solve specific tasks. The goal of this work is to segment unlabeled demonstrations into a representative set of primitives. Our proposed method differs from current approaches by taking advantage of the often neglected, mutual dependencies between the segments contained in the demonstrations and the primitives to be encoded. By exploiting this mutual dependency, we show that we can improve both the segmentation and the movement primitive library. Based on probabilistic inference our novel approach segments the demonstrations while learning a probabilistic representation of movement primitives. We demonstrate our method on two real robot applications. First, the robot segments sequences of different letters into a library, explaining the observed trajectories. Second, the robot segments demonstrations of a chair assembly task into a movement primitive library. The library is subsequently used to assemble the chair in an order not present in the demonstrations.

    @article{lirolem28021,
    month = {July},
    number = {8},
    pages = {879--894},
    volume = {36},
    year = {2017},
    journal = {International Journal of Robotics Research (IJRR)},
    publisher = {SAGE},
    author = {Rudolf Lioutikov and Gerhard Neumann and Guilherme Maeda and Jan Peters},
    title = {Learning movement primitive libraries through probabilistic segmentation},
    abstract = {Movement primitives are a well established approach for encoding and executing movements. While the primitives
    themselves have been extensively researched, the concept of movement primitive libraries has not received similar
    attention. Libraries of movement primitives represent the skill set of an agent. Primitives can be queried and sequenced
    in order to solve specific tasks. The goal of this work is to segment unlabeled demonstrations into a representative
    set of primitives. Our proposed method differs from current approaches by taking advantage of the often neglected,
    mutual dependencies between the segments contained in the demonstrations and the primitives to be encoded. By
    exploiting this mutual dependency, we show that we can improve both the segmentation and the movement primitive
    library. Based on probabilistic inference our novel approach segments the demonstrations while learning a probabilistic
    representation of movement primitives. We demonstrate our method on two real robot applications. First, the robot
    segments sequences of different letters into a library, explaining the observed trajectories. Second, the robot segments
    demonstrations of a chair assembly task into a movement primitive library. The library is subsequently used to assemble the chair in an order not present in the demonstrations.},
    keywords = {ARRAY(0x55fe0a8c7ac0)},
    url = {http://eprints.lincoln.ac.uk/28021/}
    }