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.