Prediction of Robot Grasp Robustness using Artificial Intelligence Algorithms

Predicting the quality of the robot end-effector grasp quality during an industrial robot manipulator operation can be an extremely complex task. As is often the case with such complex tasks, Artificial Intelligence methods may be applied to attempt the creation of a model if sufficient data exists. The presented dataset uses a publicly available dataset, consisting of 992632 measurements of position, torque, and velocity for each of the three joints of three fingers of the simulated end-effector. The dataset is first analyzed and pre-processed to prepare it for model training. The duplicate values are removed from the dataset, as well as the statistical outliers. Then, a multilayer perceptron (MLP) machine learning algorithm is applied to 80% of the data contained in the dataset, using the Grid Search algorithm to determine the best combination of MLP hyperparameters. As the dataset consists of torque, velocity, and speed measurements for separate joints and fingers of the tested end-effector the testing is performed to see if a subset of the inputs may be used to regress the robustness of the given grip. The normalization of the dataset is also applied, and its effect on the regression quality is tested. The results, evaluated with the coefficient of determination, show that while the best model is achieved using all the possible inputs, a satisfactory result can be obtained using only velocity and torque.The results also show that the normalization of the dataset improves the regression quality in all the observed cases.