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ACID: Action-Conditional Implicit Visual Dynamics for Deformable Object Manipulation. (arXiv:2203.06856v3 [cs.CV] UPDATED)
Aug. 9, 2022, 1:13 a.m. | Bokui Shen, Zhenyu Jiang, Christopher Choy, Leonidas J. Guibas, Silvio Savarese, Anima Anandkumar, Yuke Zhu
cs.CV updates on arXiv.org arxiv.org
Manipulating volumetric deformable objects in the real world, like plush toys
and pizza dough, bring substantial challenges due to infinite shape variations,
non-rigid motions, and partial observability. We introduce ACID, an
action-conditional visual dynamics model for volumetric deformable objects
based on structured implicit neural representations. ACID integrates two new
techniques: implicit representations for action-conditional dynamics and
geodesics-based contrastive learning. To represent deformable dynamics from
partial RGB-D observations, we learn implicit representations of occupancy and
flow-based forward dynamics. To accurately identify …
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