Abstract

Motor Primitives (MPs) are a promising approach
for the data-driven acquisition as well as for the modular and
re-usable generation of movements. However, a modular control
architecture with MPs is only effective if the MPs support
co-activation as well as continuously blending the activation
from one MP to the next. In addition, we need efficient
mechanisms to adapt a MP to the current situation. Common
approaches to movement primitives lack such capabilities or
their implementation is based on heuristics. We present a
probabilistic movement primitive approach that overcomes the
limitations of existing approaches. We encode a primitive as a
probability distribution over trajectories. The representation as
distribution has several beneficial properties. It allows encoding
a time-varying variance profile. Most importantly, it allows
performing new operations — a product of distributions for
the co-activation of MPs conditioning for generalizing the MP
to different desired targets. We derive a feedback controller
that reproduces a given trajectory distribution in closed form.
We compare our approach to the existing state-of-the art and
present real robot results for learning from demonstration.