Novel objects 3-D dense packing through robotic pushing

Robotic picking and placing systems can increase the flexibility of packing objects in a box and are attractive in many fields. However, owing to inevitable uncertainties in both the picking and placing stages, the objects cannot be placed at desired positions accurately and hence cannot be packed densely. This paper presents an additional pushing action that maximizes the packing density; i.e., after being released from the robot end, the object is moved by robotic pushing actions to arrange the packing densely. The robotic pushing strategy is determined through a deep reinforcement learning algorithm. The idea is to compress the objects toward a corner to improve the volume utilization rate by minimizing the result of a heuristic score. The learning process is implemented in simulation and the trained network is transferred to a robot system directly. Simulations and experiments are presented for the packing of regular and irregular objects to verify the proposed method.