Robotic controllers frequently operate under constraints. Often, the constraints are imperfectly or completely unknown. In this paper, the Lagrangian dynamics of a planar robot arm are expressed as a function of a globally unknown hard constraint. Laser sensors are utilized to produce estimates of local constraints. These sensors guide the end effector over the unknown object which is denoted the learning phase. The learning phase generates noisy joint position encoders and tachometers data. A extended continuous-discrete Kalman filter based estimator processes the measurements to compute an estimated parameterization of the constraint. The output of the estimator is input to a suboptimal combiner. The gradient of the estimated parameter vector is equivalent to the tactile sensory data. During the learning phase, the combiner computes a weighted combination of estimated and sensed constraints. The controller uses the constraint estimate to guide the robot arm. Thus a feedback loop is closed around the constraints. As the statistics of the estimated constraint vector become favorable compared to the stationary statistics of the sensors, the learned constraints gradually replace the need for sensory data. A block diagram of the controller, estimator, combiner, sensors, and constraints is shown. Comparative simulations are given for various combinations of ideal and noisy data.