A new neural network component, developed at University of Michigan Engineering, challenges a common assumption in robot learning: that the same general-purpose neural networks used for image recognition are equally suited to controlling physical movement.

In image classification, a neural network maps an image to a label but does not change the image. During robotic motor control, actions—like a humanoid robot moving a limb—change the robot’s physical state, which in turn changes what sensors observe next.

Robot movement and goals involve multiplicative relationships among physical variables. For example, the forces acting on one joint depend on the positions and velocities of other joints, while quantities such as mechanical power and contact multiply forces and motion.