Prediction of Robot Grasp Robustness using Artificial Intelligence Algorithms

Abstract

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.

A review of robotic grasp detection technology

“…The grasp robustness is mainly used to describe the grasp probability of a certain position or area in the image [111], and the related grasp robustness function is often used to identify the grasp pose with the highest score as the output. Therefore, learning to grasp robustness evaluation is the core method of many deep grasp detection research.…”

“…24), achieving a success rate of 71.1% and 80.0%, respectively. ŠEGOTA et al [120] used the multilayer perceptron (MLP) algorithm to regress the values of grasp robustness from a robotic grasp dataset [121] containing torque, velocity, and position information and finally obtained a high-quality regression model.

Figure 24. For different requirements, the robot has different grasp ways. A task-agnostic grasp can lift a hammer, but it may not be appropriate for particular manipulation tasks, such as sweeping or hammering.

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“…24), achieving a success rate of 71.1% and 80.0%, respectively. ŠEGOTA et al [120] used the multilayer perceptron (MLP) algorithm to regress the values of grasp robustness from a robotic grasp Figure 24. For different requirements, the robot has different grasp ways.…”

Factors Influencing the Willingness to Accept Health Behavior and Psychological Monitoring Systems in the Milieu of Information Management Technology

“…Effort expectation refers to the effort that an individual is willing to pay to achieve a certain purpose. This variable includes perceived ease of use, complexity of the system, simplicity of operation, and difficulty of using health behavior monitoring systems (Šegota et al, 2022;Zhu et al, 2022). Community influence refers to the social environment in which the user is located and its impact on the use of health or medical wearables.…”