Model predictive control is a widely used optimal control method for robot path planning and
obstacle avoidance. This control method, however, requires a system model to optimize control
over a finite time horizon and possible trajectories. Certain types of robots, such as soft
robots, continuum robots, and transforming robots, can be challenging to model, especially
in unstructured or unknown environments. Kinematic-model-free control can overcome these
challenges by learning local linear models online. This paper presents a novel perception-based
robot motion controller, the kinematic-model-free predictive controller, that is capable of controlling
robot manipulators without any prior knowledge of the robot’s kinematic structure and dynamic
parameters and is able to perform end-effector obstacle avoidance. Simulations and physical
experiments were conducted to demonstrate the ability and adaptability of the controller to
perform simultaneous target reaching and obstacle avoidance.