Kinematic redundancy refers to the difference between the number of degrees of freedom (DOF) of a robotic manipulator and the DOF needed to execute a given task ^[1]. In confined spaces where traditional robots are generally unable to operate, the most common solution is to use hyper-redundant robots (with more than 6 DOF and 3 DOF for 3D and 2D working spaces, respectively). Their large number of DOF enables them to easily maneuver around obstacles, avoid singularities and joint limits, and minimize the energy consumption ^[2][3]. However, this comes at the cost of a notoriously complex design for control and motion planning due to the high dimensionality of the problem ^[4]. Analytical methods usually require an extremely complicated characterization of the motion planning problem ^[5], which is impractical to apply, whereas numerical approaches struggle with expensive computational efforts stemming from the complexity level of the planning problem ^[6]. Although planning optimization for hyper redundant robots has been extensively researched in the last two decades, there have been no major breakthroughs in the field of motion planning for modular serial robots. Modular robotic arms are composed of a varying number of modules (links, joints, etc.) assembled together to achieve a given purpose ^[7][8]. The ability to change the assembly of the modules provides control over the level of redundancy of the robot so it can be adapted to a specific environment or task ^[8]. Although most algorithms have been developed to optimize the motion planning problem of hyper-redundant manipulators, the optimization of the redundancy itself remains an open challenge. Minimizing a manipulator’s redundancy level to fit a task enables easier and more efficient motion planning. Moreover, as the redundancy level increases, the motion plan is subjected to larger mechanical uncertainties, due to the accumulating backlash and the mechanical freedom associated with the joints. Thus, “minimal redundancy” clearly has both mechanical and computational advantages.