Using Dense Object Descriptors for Picking Cluttered General Objects with Reinforcement Learning

We propose a reinforcement learning method for picking cluttered general objects using visual descriptors with suction grasp. In this paper, we learn cluttered object descriptors (CODs), which could represent rich object structures, and use the pre-trained CODs network along with its intermediate outputs to train a picking policy. We conduct experiments to evaluate our method. Our CODs could consistently represent known and unknown cluttered general objects, which allowed for the picking policy to robustly pick cluttered general objects. The resulting policy could pick 96.69% of unseen objects that are 2X as cluttered as the training scenarios.