  We propose a framework for deformable linear object prediction. Prediction of
deformable objects (e.g., rope) is challenging due to their non-linear dynamics
and infinite-dimensional configuration spaces. By mapping the dynamics from a
non-linear space to a linear space, we can use the good properties of linear
dynamics for easier learning and more efficient prediction. We learn a locally
linear, action-conditioned dynamics model that can be used to predict future
latent states. Then, we decode the predicted latent state into the predicted
state. We also apply a sampling-based optimization algorithm to select the
optimal control action. We empirically demonstrate that our approach can
predict the rope state accurately up to ten steps into the future and that our
algorithm can find the optimal action given an initial state and a goal state.
