Robust optimization of robotic pick and place operations for deformable objects through simulation

This paper discusses various optimization schemes for partly stochastic and bound optimization, particular with application to solve robotic optimization problems, where robustness of the solutions is crucial. The use case revolves around grasping and manipulation of deformable objects. These kinds of tasks are difficult to tune to a satisfactory extent by expert users, and therefore optimization by simulation is a good alternative to achieve satisfactory solutions. In order to apply the optimization, a dynamic simulation framework has been used to model the performance of a given solution for the task. The solutions are parameterized in terms of the robot motion and the gripper configuration, and after each simulation various objective scores are determined and combined. This enables the use of various optimization strategies. Based on visual inspection of the most robust solution found, it is determined that 50 out of 50 simulations, with different meat properties, produce satisfactory manipulations.