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Sequential pulling policies to flatten and smooth fabrics have applicationsfrom surgery to manufacturing to home tasks such as bed making and foldingclothes. Due to the complexity of fabric states and dynamics, we apply deepimitation learning to learn policies that, given color (RGB), depth (D), orcombined color-depth (RGBD) images of a rectangular fabric sample, estimatepick points and pull vectors to spread the fabric to maximize coverage. Togenerate data, we develop a fabric simulator and an algorithmic supervisor thathas access to complete state information. We train policies in simulation usingdomain randomization and dataset aggregation (DAgger) on three tiers ofdifficulty in the initial randomized configuration. We present resultscomparing five baseline policies to learned policies and report systematiccomparisons of RGB vs D vs RGBD images as inputs. In simulation, learnedpolicies achieve comparable or superior performance to analytic baselines. In180 physical experiments with the da Vinci Research Kit (dVRK) surgical robot,RGBD policies trained in simulation attain coverage of 83% to 95% depending ondifficulty tier, suggesting that effective fabric smoothing policies can belearned from an algorithmic supervisor and that depth sensing is a valuableaddition to color alone. Supplementary material is available athttps://sites.google.com/view/fabric-smoothing.

Deep Imitation Learning of Sequential Fabric Smoothing From an Algorithmic Supervisor