general and compatible with arbitrary robot mechanisms and task spaces. As a downside, they can reach inaccurate or bogus results more often and have high computational costs. With the advances being made in the field of machine learning and artificial intelligence, Deep Neural Networks have arisen as a new possible approach to try to solve the IK problem. In particular, this problem can easily be formulated in a supervised learning framework, with the end-effector position and orientation as a set of inputs and the resulting joint space positions as a set of outputs. As can be seen in [3,4], many new advances have been made regarding this challenge in recent years. Feed-forward Multilayer Perceptrons (MLPs) have proven to be effective in solving the IK problem, competing alongside analytical, numerical, and heuristic methods such as FABRIK [5]. They can achieve great precision and rival other DNN models, especially when dealing with simpler datasets where orientation is not considered [6]. Convolutional Neural Networks (CNNs), although commonly used in image processing and recognition tasks, can additionally be applied to the IK problem. In [7], a network consisting of a CNN connected to a shallow neural network with dense layers was tested against a simpler MLP, yielding better accuracy and emphasizing the importance of data scaling. Similar results were found in [8] for a 6-DOF industrial manipulator. Recurrent Neural Networks (RNNs) possess a recurrent unit that acts as a sort of memory, thus allowing the model to take past inputs into account when learning from new data. This makes them well suited for dealing with data formulated as a time series, and an option to be considered for the IK problem. RNNs are used in [9] to solve the IK of a 6-DOF robotic manipulator, achieving remarkable results when compared to a backpropagation neural network using particle swarm optimization. Its usage can additionally be seen in [10] with a manipulator with one less DOF. Again, the results prove it has high efficiency and satisfies the demands of real-time computing. A number of studies exist that compare the performance of these supervised learning models. For example, ref. [11] compares an MLP with two different RNN architectures, with a Gated Recurrent Unit (GRU) RNN achieving the highest precision in the test step, as well as generating solutions faster than the other candidates. In [6], different MLP architectures are compared, with a feed-forward structure obtaining the highest accuracy for a 3-DOF manipulator when the orientation is not considered. In [12], MLPs, CNNs, and three types of RNNs are presented and compared for 3, 5, and 6-DOF robotic manipulators, proving that the Bilateral Long Short-Term Memory (BiLSTM) RNN always obtains the best results. The DNN methods are additionally compared to traditional IK approaches, showing that they can compete with them to reach any position in 3D space while avoiding singularities. Apart from supervised learning architectures, examples can be found of unsupervised learning and Deep Reinforcement Learning (DRL) techniques being applied to the IK problem. These options allow to eliminate the data generation step in the training and its associated costs. Unsupervised learning schemes include the use of Generative Adversarial Networks [13], Variational Autoencoders [14] or Graph Neural Networks [15,16]. Examples of the use of Deep Reinforcement Learning for the IK problem and the control of robotic manipulators can be found in [17,18]. In [19], the applications of curriculum learning for a DRL-based controller are presented, and this technique is found to be crucial for obtaining better results. Transformers have been applied to this problem as well, usually in the field of soft robotics [20, 21, 22]. In this paper, a DNN-based approach to solving Inverse Kinematics is proposed. No trajectories or velocities are considered in the study, as the focus is on static transformations.