ARKOMA: The Dataset to Build Neural Networks-Based Inverse Kinematics for NAO Robot Arms

Published: 23 August 2023| Version 1 | DOI: 10.17632/brg4dz8nbb.1


The dataset that we published in this data repository can be used to build neural networks-based inverse kinematics for NAO robot arms. This dataset is named ARKOMA. ARKOMA is an acronym for ARif eKO MAuridhi, all of whom are the creators of this dataset. This dataset contains input-output data pairs. In this dataset, the input data is the end-effector position and orientation in the three-dimensional cartesian space, and the output data is a set of joint angular positions. These joint angular positions are in radians. For further applications, this dataset was split into the training dataset, validation dataset, and testing dataset. The training dataset is used to train neural networks. The validation dataset is utilized to validate neural networks’ performance during the training process. Meanwhile, the testing dataset is employed after the training process to test the performance of trained neural networks. From a set of 10000 data, 60% of data was allocated for the training dataset, 20% of data for the validation dataset, and the other 20% of data for the testing dataset. It should be noted, this dataset is compatible with NAO H25 v3.3 or later.



Institut Teknologi Sepuluh Nopember


Artificial Intelligence, Control Systems, Robotics, Kinematics, Deep Learning


Kementerian Riset Teknologi Dan Pendidikan Tinggi Republik Indonesia