Inspired by Skinner’s operant conditioning theory, an operant conditioning learning model is presented to deal with the autonomous learning problem in cognitive robotics. The model is described by nine elements, including the space set, the action set, the bionic learning function and the system entropy etc. A notion “negative ideal rate” is defined to compute the orientation function. The OCLM is applied to solve obstacle avoidance and navigation problems for mobile robots. The experiment results show that the robot based on the model can autonomously learn how to arrive at the goal in a collision-free way through interaction with the environment, and show the effectiveness of the proposed model.