  In contexts such as teleoperation, robot reprogramming,
human-robot-interaction, and neural prosthetics, conveying movement commands to
a robotic platform is often a limiting factor. Currently, many applications
rely on joint-angle-by-joint-angle prescriptions. This inherently requires a
large number of parameters to be specified by the user that scales with the
number of degrees of freedom on a platform, creating high bandwidth
requirements for interfaces. This paper presents an efficient representation of
high-level, spatial commands that specifies many joint angles with relatively
few parameters based on a spatial architecture that is judged favorably by
human viewers. In particular, a general method for labeling connected platform
linkages, generating a databank of user-specified poses, and mapping between
high-level spatial commands and specific platform static configurations are
presented. Thus, this architecture is ``platform-invariant'' where the same
high-level, spatial command can be executed on any platform. This has the
advantage that our commands have meaning for human movers as well. In order to
achieve this, we draw inspiration from Laban/Bartenieff Movement Studies, an
embodied taxonomy for movement description. The architecture is demonstrated
through implementation on 26 spatial directions for a Rethink Robotics Baxter,
an Aldebaran NAO, and a KUKA youBot. User studies are conducted to validate the
claims of the proposed framework.
