ISER 2016: Experiments with hierarchical reinforcement learning of multiple grasping policies

Robotic grasping has attracted considerable interest, but it still remains a challenging task. The data-driven approach is a promising solution to the robotic grasping problem; this approach leverages a grasp dataset and generalizes grasps for various objects. However, these methods often depend on the quality of the given datasets, which are not trivial to obtain with sufficient quality. Although reinforcement learning approaches have been recently used to achieve autonomous collection of grasp datasets, the existing algorithms are often limited to specific grasp types. In this paper, we present a framework for hierarchical reinforcement learning of grasping policies. In our framework, the lowerlevel hierarchy learns multiple grasp types, and the upper-level hierarchy learns a policy to select from the learned grasp types according to a point cloud of a new object. Through experiments, we validate that our approach learns grasping by constructing the grasp dataset autonomously. The experimental results show that our approach learns multiple grasping policies and generalizes the learned grasps by using local point cloud information.

  • T. Osa, J. Peters, and G. Neumann, “Experiments with hierarchical reinforcement learning of multiple grasping policies,” in Proceedings of the International Symposium on Experimental Robotics (ISER), 2016.
    [BibTeX] [Abstract] [Download PDF]

    Robotic grasping has attracted considerable interest, but it still remains a challenging task. The data-driven approach is a promising solution to the robotic grasping problem; this approach leverages a grasp dataset and generalizes grasps for various objects. However, these methods often depend on the quality of the given datasets, which are not trivial to obtain with sufficient quality. Although reinforcement learning approaches have been recently used to achieve autonomous collection of grasp datasets, the existing algorithms are often limited to specific grasp types. In this paper, we present a framework for hierarchical reinforcement learning of grasping policies. In our framework, the lowerlevel hierarchy learns multiple grasp types, and the upper-level hierarchy learns a policy to select from the learned grasp types according to a point cloud of a new object. Through experiments, we validate that our approach learns grasping by constructing the grasp dataset autonomously. The experimental results show that our approach learns multiple grasping policies and generalizes the learned grasps by using local point cloud information.

    @inproceedings{lirolem26735,
    author = {T. Osa and J. Peters and G. Neumann},
    booktitle = {Proceedings of the International Symposium on Experimental Robotics (ISER)},
    month = {April},
    title = {Experiments with hierarchical reinforcement learning of multiple grasping policies},
    year = {2016},
    keywords = {ARRAY(0x558aaed335a8)},
    url = {http://eprints.lincoln.ac.uk/26735/},
    abstract = {Robotic grasping has attracted considerable interest, but it
    still remains a challenging task. The data-driven approach is a promising
    solution to the robotic grasping problem; this approach leverages a
    grasp dataset and generalizes grasps for various objects. However, these
    methods often depend on the quality of the given datasets, which are not
    trivial to obtain with sufficient quality. Although reinforcement learning
    approaches have been recently used to achieve autonomous collection
    of grasp datasets, the existing algorithms are often limited to specific
    grasp types. In this paper, we present a framework for hierarchical reinforcement
    learning of grasping policies. In our framework, the lowerlevel
    hierarchy learns multiple grasp types, and the upper-level hierarchy
    learns a policy to select from the learned grasp types according to a point
    cloud of a new object. Through experiments, we validate that our approach
    learns grasping by constructing the grasp dataset autonomously.
    The experimental results show that our approach learns multiple grasping
    policies and generalizes the learned grasps by using local point cloud
    information.}
    }

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