Abstract: 3D bin packing problem is a combinatorial optimization problem that needs packing a certain number of objects and maximizing the volume utilization under the constraints of volume limit and stability limit. 3D packing problem is a NP hard problem. Heuristic algorithm is usually used to find the best position to place the object. When robot is used for packing, manipulation uncertainties should be handled. For example, the collisions between the manipulator and the surroundings, and the planning errors of the manipulator motion trajectories may make some optimal poses infeasible. Thus, the object can only be dropped from a higher place or placed near the optimal pose. The uncertainties of robot in grasping, recognition and placing also lead to the error between the real object position and the planned one. Therefore, an optimization method for robot 3D packing via pushing is proposed based on deep reinforcement learning. Aiming at minimizing the score of the heuristic algorithm for the positions of objects in the bin, robot can reorganize the positions of placed objects via pushing. Meanwhile, the objects are compressed towards a corner to make more space and improve the volume utilization rate of packing.

Key words: reinforcement learning, deep learning, 3D bin packing, robot push