Paper Info
Title
Deep Imitation Learning of Sequential Fabric Smoothing From an Algorithmic Supervisor
Abstract
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 vectors to spread the fabric to maximize coverage. To generate data, we develop a fabric simulator and an algorithmic supervisor that has access to complete state information. We train policies in simulation using domain randomization and dataset aggregation (DAgger) on three tiers of difficulty in the initial randomized configuration. We present results comparing five baseline policies to learned policies and report systematic comparisons of RGB vs D vs RGBD images as inputs. In simulation, learned policies achieve comparable or superior performance to analytic baselines. In 180 physical experiments with the da Vinci Research Kit (dVRK) surgical robot, RGBD policies trained in simulation attain coverage of 83% to 95% depending on difficulty tier, suggesting that effective fabric smoothing policies can be learned from an algorithmic supervisor and that depth sensing is a valuable addition to color alone. Supplementary material is available at https://sites.google.com/view/fabric-smoothing.
Year
DOI
Venue
2020
10.1109/IROS45743.2020.9341608
IROS
DocType
Citations 
PageRank 
Conference
1
0.34
References 
Authors
0
14
Name
Order
Citations
PageRank
Daniel Seita112.03
Aditya Ganapathi210.68
Ryan Hoque312.03
Minho Hwang474.18
Edward Cen510.34
Ajay Kumar Tanwani6669.07
Ashwin Balakrishna746.80
Brijen Thananjeyan8133.93
Jeffrey Ichnowski979.65
Nawid Jamali1072.55
Katsu Yamane1142.12
Soshi Iba12707.27
John Canny13121231786.38
Ken Goldberg143785369.80