Machine learning with industrial robots: exploring the impact of joint angles on Cartesian coordinates using explainable AI

Abstract

This study uses Explainable Artificial Intelligence techniques to reveal the complex relationship between joint angles and Cartesian coordinates in the context of industrial robotic arms. By using machine learning and Explainable Artificial Intelligence algorithms, it is aimed to distinguish the dominant effect of individual joint angles on the x, y and z coordinates of the robotic end effector. Various machine learning algorithms have been applied on the data set and performance outputs have been obtained. According to these performance results, it has been observed that the RandomForest algorithm is more suitable for our study than other models with its low mean square error and high r-square score. Along with the selected machine learning algorithm, the data set was tried to be explained by passing it through SHapley Additive exPlanations (SHAP), Descriptive Machine Learning EXplanations (DALEX) and Explain Like I’m 5 (ELI5) models, which are Explainable Artificial Intelligence models. It has been observed that the SHAP model explains the effects of joint angles on Cartesian coordinates more consistently than other models, with an average sensitivity of 0.0125 value range.Our findings shed light on the explainability aspect of AI models and provide valuable information about the fundamental mechanisms governing the complex movements of industrial robot arms. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024