Tracking Fast Trajectories with a Deformable Object using a Learned Model
James A. Preiss,David Millard,Tao Yao,Gaurav S. Sukhatme,James A. Preiss,David Millard,Tao Yao,Gaurav S. Sukhatme
We propose a method for robotic control of deformable objects using a learned nonlinear dynamics model. After collecting a dataset of trajectories from the real system, we train a recurrent neural network (RNN) to approximate its input-output behavior with a latent state-space model. The RNN internal state is low-dimensional enough to enable realtime nonlinear control methods. We demonstrate a clo...
