Extracting and generalizing primitive actions from sparse demonstration

Here we describe a parameter-driven solution for generating novel yet similar movements from a sparse example set obtained through observation. In our experiments, we present an algorithm where a humanoid can learn movement trajectories demonstrated by a person with intuitive parameters describing the start and end points of different motion trajectory segments. These segments are automatically detected and grouped based on straightforward data-driven metrics. After identifying groups of primitives, we use a linear approximation framework to build a representation based on relevant task features (segment start and end points) where radial basis functions(RBFs) are used to approximate the unknown nonlinear characteristics describing a trajectory. The solution is accomplished on-line and requires no interaction. With this approach a humanoid can learn from only a few examples, and quickly produce new movements.