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