Next we discuss how we are learning . It is important to remember that the above methods are very general, and have been applied to robotics learning problems in specific ways. However, they have also been successfully applied, in different variations, to non-robotics problems. Thus, do not assume that a particular method is limited in a give way because that is how it is used in robotics. All of these and more methods will be covered in detail in the Nov 17th lecture, but today we will briefly go over the general principles, and then tie those to the robot control architectures we have covered.
Reinforcement learning is currently the most popular approach to learning on mobile robots.
All of these properties make learning on physical robots very difficult. Learning algorithms have been developed for partially observable environments, and while they are more realistic, they require even more time to converge. In general, managing uncertainty is very hard in learning. Reinforcement learning is very popular because it allows a robot to learn on its own , using its own experiences as reinforcement . For example, if a robot hits a wall, it can count that as negative reinforcement, and avoid whatever it was it did that resulted in hitting a wall.