Deep Imitation Learning of Sequential Fabric Smoothing From an Algorithmic Supervisor
Daniel Seita,Aditya Ganapathi,Ryan Hoque,Minho Hwang,Edward Cen,Ajay Kumar Tanwani,Ashwin Balakrishna,Brijen Thananjeyan,Jeffrey Ichnowski,Nawid Jamali,Katsu Yamane,Soshi Iba,John Canny,Ken Goldberg,Daniel Seita,Aditya Ganapathi,Ryan Hoque,Minho Hwang,Edward Cen,Ajay Kumar Tanwani,Ashwin Balakrishna,Brijen Thananjeyan,Jeffrey Ichnowski,Nawid Jamali,Katsu Yamane,Soshi Iba,John Canny,Ken Goldberg
Sequential pulling policies to flatten and smooth fabrics have applications from surgery to manufacturing to home tasks such as bed making and folding clothes. Due to the complexity of fabric states and dynamics, we apply deep imitation learning to learn policies that, given color (RGB), depth (D), or combined color-depth (RGBD) images of a rectangular fabric sample, estimate pick points and pull ...
