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Title: FabricFlowNet: Bimanual Cloth Manipulation with a Flow-based Policy
We address the problem of goal-directed cloth manipulation, a chal- lenging task due to the deformability of cloth. Our insight is that optical flow, a technique normally used for motion estimation in video, can also provide an effective representation for corresponding cloth poses across observation and goal images. We introduce FabricFlowNet (FFN), a cloth manipulation policy that leverages flow as both an input and as an action representation to improve performance. FabricFlowNet also elegantly switches between dual-arm and single- arm actions based on the desired goal. We show that FabricFlowNet significantly outperforms state-of-the-art model-free and model-based cloth manipulation policies. We also present real-world experiments on a bimanual system, demonstrating effective sim-to-real transfer. Finally, we show that our method generalizes when trained on a single square cloth to other cloth shapes, such as T-shirts and rectangular cloths. Video and other supplementary materials are available at: https://sites.google.com/view/fabricflownet.  more » « less
Award ID(s):
2046491 1849154
PAR ID:
10313064
Author(s) / Creator(s):
; ; ; ;
Date Published:
Journal Name:
Conference on Robot Learning
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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