  Although, in the task of grasping via a data-driven method, closed-loop
feedback and predicting 6 degrees of freedom (DoF) grasp rather than
conventionally used 4DoF top-down grasp are demonstrated to improve performance
individually, few systems have both. Moreover, the sequential property of that
task is hardly dealt with, while the approaching motion necessarily generates a
series of observations. Therefore, this paper synthesizes three approaches and
suggests a closed-loop framework that can predict the 6DoF grasp in a heavily
cluttered environment from continuously received vision observations. This can
be realized by formulating the grasping problem as Hidden Markov Model and
applying a particle filter to infer grasp. Additionally, we introduce a novel
lightweight Convolutional Neural Network (CNN) model that evaluates and
initializes grasp samples in real-time, making the particle filter process
possible. The experiments, which are conducted on a real robot with a heavily
cluttered environment, show that our framework not only quantitatively improves
the grasping success rate significantly compared to the baseline algorithms,
but also qualitatively reacts to a dynamic change in the environment and cleans
up the table.
