## 2 Related Work  
Model-based controllers have been the predominant method of control for robot manipulation. The Jacobian of a robot, which relates the joint velocities to the end-effector velocity, is calculated prior to control [6], after which it is controlled using forward and inverse kinematics [7]. Using such models imposes many limitations [8] and assumes the Denavit-Hartenberg parameters, that define the robots kinematics, to be static. One example of model-based control is visual servoing that uses an external camera to calculate the required end-effector velocity to reach a desired target [9]. However, this assumption falls apart in the case of soft robots, when robots are damaged, or when robots transform or evolve in any way, such as altering the end effector by changing its gripper. Therefore, model-based conventional controllers are accurate but not adaptable.  
Other researchers performed model-free control by approximating a Jacobian that is later used to actuate a robot. Researchers in Reference [10] controlled a deformable manipulator and in Reference [11] controlled a soft manipulator by approximating its Jacobian model. Other researchers controlled a dynamically-undefined robot by first estimating the robot's dynamic parameters and then controlled a robot using conventional methods [12]. Learning methods, such as motor babbling, that start with no predefined model of a robot's kinodynamics are used to approximate a model to control robots [13]. Other researchers use machine learning [14] and reinforcement learning algorithms [15] to control robots.  
On the other hand, researchers in Reference [16] directly controlled current motors to actuate an unmanned underwater vehicle by means of deterministic artificial intelligence where uncertainties are ignored by making use of first principles. Researchers in Reference [17] controlled a SCARA robot's position through motor voltages using extended state observer to estimate system dynamics. Other researchers studied hybrid model-free and model-based approaches to increase the performance of robot navigation [18]. However, most of these learning approaches require encoder feedback for estimating the robot's pose.  
The classical kinematic-model-free position controllers, the Encoderless [3] and Kinematic-Free [4] controllers, do not require any prior model of the robot. They operate by generating psuedo-random actuation signals that actuate a planar 2-degrees-of-freedomrobot's joints causing a positional displacement on the robot's end effector. The effect on the robot's end effector is observed using an external camera and is recorded. The recorded data are then used to generate a local linear model that actuates the robot's end effector towards a given target. Any discrepancies will re-trigger another exploratory phase. The classical kinematic-model-free controller was then extended to simultaneously control the position and orientation of a 3-degrees-of-freedom robot's end effector [5] using locally-weighted dual quaternions. These kinematic-model-free controllers, however, are unable to resolve redundancies in redundant robots, such as manipulators with high degrees of freedom [19] and continuum robots [20].