ROBOTS DEVELOPING MUSCLE MEMORY


THE MANAGER OF THE FUTURE


Researchers at ARL are exploring methods for robots to learn and use models that enable faster autonomy by assessing when and under what conditions different methods of sensing perform well or poorly. (Image by iLexx/iStock)


experience a player has about how to move his or her body to play the game, particularly for the position. Te execution of an assumed mental model is called “feed forward control.” A men- tal model that is incorrect or incomplete, such as one used by an inexperienced player, will reduce accuracy and repeatability and require more time to complete a task.


We can assume that even professional baseball players would need significant time to adjust if they were magically trans- ported to play on the moon, where gravity is much weaker and air resistance is nonexistent. Similarly, another instance of incorrect models can be observed in the clumsy and uncoor- dinated movements of quickly growing children; their mental models of how to relate to the world must constantly change and adapt because they are changing. Nevertheless, humans are quite resilient to change and, with practice, they can adapt to perform well in new situations.


A major focus of much current research at the U.S. Army Research Laboratory (ARL) is creating a robot like Rosie,


104 Army AL&T Magazine January-March 2017


capable of learning and executing tasks with the best preci- sion and speed possible, given what we know about our own abilities.


NOT QUITE ‘INFINITE IN FACULTY’ In general, we can say that Rosie-like robot performance is pos- sible given sufficient advances in the areas of sensing, modeling self-motion and modeling interactions with the world.


Robots “perceive” the world around them using myriad inte- grated sensors. Tese sensors include laser range scanners and acoustic ranging, which provide the distance from the robot to obstacles; cameras that permit the robot to see the world, similar to our own eyes; inertial measurement sensing that includes rate gyroscopes, which sense the rate of change of the orientation of the robotic device; and accelerometers, which sense acceleration and gravity, giving the robot an “inner ear” of sorts. All these methods of sensing the world provide different types of informa- tion about the robot’s motion or location in the environment.


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