Generalizing Movement Primitives to New Situations

Abstract

Although motor primitives (MPs) have been studied extensively, much less attention has been devoted to studying their generalization to new situations. To cope with varying conditions, a MP’s policy encoding must support generalization over task parameters to avoid learning separate primitives for each condition. Local and linear parameterized models have been proposed to interpolate over task parameters to provide limited generalization. In this paper, we present a global parametric motion primitive (GPDMP) which allows generalization beyond local or linear models. Primitives are modelled using a linear basis function model with global non-linear basis functions. The model is constructed from initial non-parametric primitives found using a single human demonstration and subsequent episodes of reinforcement learning to adapt the demonstrated skill to other task parameters. The initial models are then used to optimize the parameters of the global parametric model. Experiments with a ball-in-a-cup task with varying string lengths show that GPDMP allows greatly improved extrapolation compared to earlier local or linear models.