Self-Supervised Learning of State Estimation for Manipulating Deformable Linear Objects
Self-Supervised Learning of State Estimation for Manipulating Deformable Linear Objects
We demonstrate model-based, visual robot manipulation of deformable linear objects. Our approach is based on a state-space representation of the physical system that the robot aims to control. This choice has multiple advantages, including the ease of incorporating physics priors in the dynamics model and perception model, and the ease …