Generalizing behavior obtained 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, a humanoid learns to represent movement trajectories demonstrated by a person with intuitive parameters describing the start and end points of different motion trajectory segments. These segments are automatically produced based on changes in curvature. After rebinning to equate similar segments across the samples, 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 non-linear 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.