In contexts such as teleoperation, robot reprogramming, and human-robot-interaction, and neural prosthetics, conveying spatial 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 thesis presents an efficient representation of high-level, spatial commands that specifies many joint angles with relatively few parameters based on a spatial architecture. To this end, an expressive command architecture is proposed that allows pose generation of simple motion primitives. 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. Further, this architecture is platform- invariant where the same high-level, spatial command can have meaning on any platform. This has the particular 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 final architecture is implemented for twenty-six spatial directions on a Rethink Robotics Baxter and an Aldebaran NAO. Two user studies have been conducted to validate the effectiveness of the proposed framework. Lastly, a workload metric is proposed to quantitative assess the usability of a machine interface.
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An embodied, platform-invariant architecture for robotic spatial commands
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