Robotic Paper Wrapping by Learning Force Control

Robotic packaging using wrapping paper poses significant challenges due to the material’s complex deformation properties. The packaging process itself involves multiple steps, primarily categorized as folding the paper or creating creases. Small deviations in the robot’s arm trajectory or force vector can lead to tearing or wrinkling of the paper, exacerbated by the variability in material properties. This study introduces a novel framework that combines imitation learning and reinforcement learning to enable a robot to perform each step of the packaging process efficiently. The framework allows the robot to follow approximate trajectories of the tool-center point (TCP) based on human demonstrations while optimizing force control parameters to prevent tearing or wrinkling, even with variable wrapping paper materials. For training, we used a dataset of 480k trajectories expanded from 10 human demonstration trajectories for each subtask. Additionally, position control parameters are trained in the simulation environment for 110k steps, followed by the training of force control parameters on the real robot for 100k steps. The proposed method was validated through ablation studies, which demonstrated that the task success rate improved significantly from 12.5% to 95%, while the rates of tearing and wrinkling were reduced from 51% to 5% and from 52% to 5%, respectively. Furthermore, the force control strategy proved to be adaptable across different wrapping paper materials and robust against variations in the size of the target object.