Distal learning applied to biped robots

In order for biped robots to handle a variety of tasks, the robot must be able to traverse different terrains. Different terrains require different walking gaits, and if all these gaits must be programmed by human operators then this programming can be a very large and time consuming process. If, however, the robot has the capability to automatically generate different gaits when placed on unfamiliar terrain, then the need to program many different gaits is eliminated. This paper looks at a method to generate gaits based on distal supervised learning. This method incorporates a forward model of the robot dynamics and uses it to convert stability information into information on how to adjust the robot´s joints so as to regain stability. The method is tested with a simulation of the SD-2 biped robot