Ever see a lizard effortlessly run up a wall?
Like most vertebrates, lizards are able to quickly adapt to new environments in a robust way thanks to a special type of movement generator. The idea is that a high-level planner (the brain) is responsible for determining the key characteristics of a movement such as the position that needs to be reached by a limb or the amplitude and frequency with which the limbs should perform rhythmic motions. These high-level commands then serve as an input to motion primitives responsible for activating muscles in the correct sequence. Motion primitives are typically organized at the spinal level through neural networks called central pattern generators (CPGs).
This control architecture has many advantages for robotics. First, once the motion primitives are designed, only high-level commands are required to control the entire motion of the robot. Therefor, instead of planning the positions of all joints, the motion planner only needs to issue high-level goals such as “reach there” or “move your arm rhythmically with this amplitude and this frequency”. This greatly reduces the complexity of planning motions for robots with many degrees of freedom. Furthermore, CPGs are very fast, have low computational cost and can be modulated by sensory feedback in order to obtain adaptive behaviors.
Using this control architecture, Degallier et al. were able to turn the iCub humanoid seen in the video below into an on-demand drummer. Random users at a robotics conference were able to change on-line a score that the iCub was playing or test how well it could adapt when its drums were moved. To show the generality of their approach, they then applied the same architecture to make the iCub crawl and reach for objects. Although one behaviour was rhythmic (crawling) and the other discrete (reaching), the robot was easily able to switch between the two.