2 new IROS papers accepted!

  • J. Pajarinen, V. Kyrki, M. Koval, S. Srinivasa, J. Peters, and G. Neumann, “Hybrid control trajectory optimization under uncertainty,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2017.
    [BibTeX] [Abstract] [Download PDF]

    Trajectory optimization is a fundamental problem in robotics. While optimization of continuous control trajectories is well developed, many applications require both discrete and continuous, i.e. hybrid controls. Finding an optimal sequence of hybrid controls is challenging due to the exponential explosion of discrete control combinations. Our method, based on Differential Dynamic Programming (DDP), circumvents this problem by incorporating discrete actions inside DDP: we first optimize continuous mixtures of discrete actions, and, subsequently force the mixtures into fully discrete actions. Moreover, we show how our approach can be extended to partially observable Markov decision processes (POMDPs) for trajectory planning under uncertainty. We validate the approach in a car driving problem where the robot has to switch discrete gears and in a box pushing application where the robot can switch the side of the box to push. The pose and the friction parameters of the pushed box are initially unknown and only indirectly observable.

    @inproceedings{lirolem28257,
    author = {J. Pajarinen and V. Kyrki and M. Koval and S Srinivasa and J. Peters and G. Neumann},
    title = {Hybrid control trajectory optimization under uncertainty},
    month = {September},
    booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
    year = {2017},
    url = {http://eprints.lincoln.ac.uk/28257/},
    keywords = {ARRAY(0x55fe0a793a70)},
    abstract = {Trajectory optimization is a fundamental problem in robotics. While optimization of continuous control trajectories is well developed, many applications require both discrete and continuous, i.e. hybrid controls. Finding an optimal sequence of hybrid controls is challenging due to the exponential explosion of discrete control combinations. Our method, based on Differential Dynamic Programming (DDP), circumvents this problem by incorporating discrete actions inside DDP: we first optimize continuous mixtures of discrete actions, and, subsequently force the mixtures into fully discrete actions. Moreover, we show how our approach can be extended to partially observable Markov decision processes (POMDPs) for trajectory planning under uncertainty. We validate the approach in a car driving problem where the robot has to switch discrete gears and in a box pushing application where the robot can switch the side of the box to push. The pose and the friction parameters of the pushed box are initially unknown and only indirectly observable.}
    }

  • A. Paraschos, R. Lioutikov, J. Peters, and G. Neumann, “Probabilistic prioritization of movement primitives,” IEEE Robotics and Automation Letters, vol. PP, iss. 99, 2017.
    [BibTeX] [Abstract] [Download PDF]

    Movement prioritization is a common approach to combine controllers of different tasks for redundant robots, where each task is assigned a priority. The priorities of the tasks are often hand-tuned or the result of an optimization, but seldomly learned from data. This paper combines Bayesian task prioritization with probabilistic movement primitives to prioritize full motion sequences that are learned from demonstrations. Probabilistic movement primitives (ProMPs) can encode distributions of movements over full motion sequences and provide control laws to exactly follow these distributions. The probabilistic formulation allows for a natural application of Bayesian task prioritization. We extend the ProMP controllers with an additional feedback component that accounts inaccuracies in following the distribution and allows for a more robust prioritization of primitives. We demonstrate how the task priorities can be obtained from imitation learning and how different primitives can be combined to solve even unseen task-combinations. Due to the prioritization, our approach can efficiently learn a combination of tasks without requiring individual models per task combination. Further, our approach can adapt an existing primitive library by prioritizing additional controllers, for example, for implementing obstacle avoidance. Hence, the need of retraining the whole library is avoided in many cases. We evaluate our approach on reaching movements under constraints with redundant simulated planar robots and two physical robot platforms, the humanoid robot ?iCub? and a KUKA LWR robot arm.

    @article{lirolem27901,
    journal = {IEEE Robotics and Automation Letters},
    month = {July},
    booktitle = {, Proceedings of the International Conference on Intelligent Robot Systems, and IEEE Robotics and Automation Letters (RA-L)},
    year = {2017},
    number = {99},
    publisher = {IEEE},
    author = {Alexandros Paraschos and Rudolf Lioutikov and Jan Peters and Gerhard Neumann},
    title = {Probabilistic prioritization of movement primitives},
    volume = {PP},
    keywords = {ARRAY(0x55fe0a78fdc0)},
    abstract = {Movement prioritization is a common approach
    to combine controllers of different tasks for redundant robots,
    where each task is assigned a priority. The priorities of the
    tasks are often hand-tuned or the result of an optimization,
    but seldomly learned from data. This paper combines Bayesian
    task prioritization with probabilistic movement primitives to
    prioritize full motion sequences that are learned from demonstrations.
    Probabilistic movement primitives (ProMPs) can
    encode distributions of movements over full motion sequences
    and provide control laws to exactly follow these distributions.
    The probabilistic formulation allows for a natural application of
    Bayesian task prioritization. We extend the ProMP controllers
    with an additional feedback component that accounts inaccuracies
    in following the distribution and allows for a more
    robust prioritization of primitives. We demonstrate how the
    task priorities can be obtained from imitation learning and
    how different primitives can be combined to solve even unseen
    task-combinations. Due to the prioritization, our approach can
    efficiently learn a combination of tasks without requiring individual
    models per task combination. Further, our approach can
    adapt an existing primitive library by prioritizing additional
    controllers, for example, for implementing obstacle avoidance.
    Hence, the need of retraining the whole library is avoided in
    many cases. We evaluate our approach on reaching movements
    under constraints with redundant simulated planar robots and
    two physical robot platforms, the humanoid robot ?iCub? and
    a KUKA LWR robot arm.},
    url = {http://eprints.lincoln.ac.uk/27901/}
    }