Due to the inherent uncertainty in their deformability during motion,
previous methods in deformable object manipulation, such as rope and cloth,
often required hundreds of real-world demonstrations to train a manipulation
policy for each object, which hinders their applications in our ever-changing
world. To address this issue, we introduce GenDOM, a framework that allows the
manipulation policy to handle different deformable objects with only a single
real-world demonstration. To achieve this, we augment the policy by
conditioning it on deformable object parameters and training it with a diverse
range of simulated deformable objects so that the policy can adjust actions
based on different object parameters. At the time of inference, given a new
object, GenDOM can estimate the deformable object parameters with only a single
real-world demonstration by minimizing the disparity between the grid density
of point clouds of real-world demonstrations and simulations in a
differentiable physics simulator. Empirical validations on both simulated and
real-world object manipulation setups clearly show that our method can
manipulate different objects with a single demonstration and significantly
outperforms the baseline in both environments (a 62% improvement for in-domain
ropes and a 15% improvement for out-of-distribution ropes in simulation, as
well as a 26% improvement for ropes and a 50% improvement for cloths in the
real world), demonstrating the effectiveness of our approach in one-shot
deformable object manipulation.

GenDOM 框架通过将策略与可变形物体参数相关联并使用各种模拟可变形物体进行训练，从而使策略能够处理不同的可变形物体，而只需进行一次真实世界演示。此方法在模拟和真实世界环境中进行了实证验证，可通过单次演示处理不同对象并在性能上显著优于基线方法，证明了该方法在一次性可变形物体操作中的有效性。