the robot to reach a singularity, causing operational issues. The problem can become more complicated if there are obstacles present in the workspace. In this case, given a desired pose for the end-effector \(X_{d}\in SE(3)\) and a set of constraints, a sequence of configurations could be devised for the manipulator so that the robot can follow them and reach the target without colliding (motion planning [3]). The alternative to motion planning would be to design a controller so that, given the current state of the manipulator and the obstacles, the best immediate configuration is chosen so that the robot gets closer to the target while staying away from the obstacles (motion control [4]). The IK problem can be classically solved using analytical, numerical, or meta-heuristic algorithms. Analytical solutions are generally the most precise, so advances are being made in this field to find new methods [5; 6; 7]. The computing time of the analytical IK solutions is faster than the numerical approach and real-time capable, but it is only available for specific robot kinematics. Numerical methods can be divided into three categories: Jacobian, Newton and heuristic methods [8]. They are the preferred option when dealing with redundant manipulators. However, they often reach singularities and have high computational costs (except for heuristic methods, which tend to be faster but cannot adapt to global constraints and sometimes end in unnatural positions). New advances regarding numerical methods can be seen in [9; 10]. Finally, meta-heuristic algorithms [11] such as genetic algorithms [12] and particle swarm optimization [13] represent another possible means to solve the IK problem. However, these methods are unsuitable for dynamic environments and require high computation time. Motion control takes the manipulator and environment state as an input and decides the best immediate action according to a function. In the presence of obstacles, this function will contain the distance to the target as an objective to minimize and the distance to the obstacles as an objective to maximize. Motion control for obstacle avoidance can be achieved by means of the Jacobian pseudo-inverse [14]. On the downside, this method can lead to singularities and relies on a distance algorithm such as [15]. Artificial potential fields [16] represent another option, which consist of a set of forces that attract the manipulator toward the goal and repel it from obstacle regions. They can be used in real-time controllers. A more recent alternative is the Jacobian transpose [17], which does not need to calculate inverse Jacobian matrices or to know the dynamics of manipulators. Motion planning takes the manipulator and environment state as an input and outputs a sequence of actions according to a set of parameters and constraints. In the presence of obstacles, these constraints will make sure that the robot does not make movements that would take it close to colliding with them or with itself. Motion planning can be achieved via potential fields [18] as well. However, the preferred options to plan trajectories while avoiding collisions are random sampling based methods such as Rapidly Exploring Random Trees (RRT) [19]. RRT has probabilistic completeness, perfect expansion, and a fast exploring speed. However, it cannot deal with dynamic unstructured environments in which many obstacles are distributed randomly. Another sampling based method is Probabilistic Roadmaps (PRM) [20]. The algorithm abstracts the configuration space by selecting a number of points in it and connecting them to form an undirected graph. The path search problem in the free space is transformed to a graph search problem. In this article, the approach to the IK, motion control and motion planning problems using Deep Neural Networks (DNN) is studied. Neural networks are capable of learning complex functions from data, and then generalize to new points outside of it.