The flapping flight of bats can serve as an inspiration for flapping-wing air vehicles. Obtaining an understanding of bat flight requires detailed, occlusion-free kinematics data that can only be collected using large numbers of cameras. Here, we have explored the use of low-cost cameras with low frame rates that result in nonlinear, large-baseline motions in image space. To create a better model for predicting the motion of features under these circumstances, we have applied Gaussian Process Dynamic Modeling (GPDM) to manually digitized flight data in order to learn a lower dimensional manifold near which the motion evolves. The primary contribution of this work is the first nonlinear dimensionality reduction for the representation of bat
flight.