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Robotic fabric manipulation has applications in home robotics, textiles,senior care and surgery. Existing fabric manipulation techniques, however, aredesigned for specific tasks, making it difficult to generalize across differentbut related tasks. We extend the Visual Foresight framework to learn fabricdynamics that can be efficiently reused to accomplish different fabricmanipulation tasks with a single goal-conditioned policy. We introduceVisuoSpatial Foresight (VSF), which builds on prior work by learning visualdynamics on domain randomized RGB images and depth maps simultaneously andcompletely in simulation. We experimentally evaluate VSF on multi-step fabricsmoothing and folding tasks against 5 baseline methods in simulation and on theda Vinci Research Kit (dVRK) surgical robot without any demonstrations at trainor test time. Furthermore, we find that leveraging depth significantly improvesperformance. RGBD data yields an 80% improvement in fabric folding success rateover pure RGB data. Code, data, videos, and supplementary material areavailable at https://sites.google.com/view/fabric-vsf/.

VisuoSpatial Foresight for Multi-Step, Multi-Task Fabric Manipulation