Human motion calls upon embodied strategies, which can be difficult to replicate in teleoperation architectures. This paper presents a teleoperation method that centers around the Space component of Laban Movement Analysis and may improve the dynamic complexity of teleoperation commands, allowing a trained user to command multiple joint angles at one time via a large database of stored poses, which are indexed by Space parameters. In this paper, this method is compared to a benchmark method, utilizing a joint-by-joint manner of control on a Rethink Robotics Baxter with compliant limbs using a Microsoft Xbox controller. Across four tasks with a trained operator, analysis of the number of active joints at a given point in time and time to completion emphasize the utility that comes with the proposed method. In particular, for the two presented static tasks, the average number of joint angles moving at one time improves and completion times reduce for the proposed method. Plots of behavior show additional qualitative differences in operator strategies and resulting motion, which are also discussed. Future work will extend this initial demonstration to more formal trials with multiple operators. This method may help achieve more fluid, continuous, and improvised motion in teleoperation of robots via gamepads as are currently used in disaster response platforms.