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Due to the inherent uncertainty in their deformability during motion,previous methods in rope manipulation often require hundreds of real-worlddemonstrations to train a manipulation policy for each rope, even for simpletasks such as rope goal reaching, which hinder their applications in ourever-changing world. To address this issue, we introduce GenORM, a frameworkthat allows the manipulation policy to handle different deformable ropes with asingle real-world demonstration. To achieve this, we augment the policy byconditioning it on deformable rope parameters and training it with a diverserange of simulated deformable ropes so that the policy can adjust actions basedon different rope parameters. At the time of inference, given a new rope,GenORM estimates the deformable rope parameters by minimizing the disparitybetween the grid density of point clouds of real-world demonstrations andsimulations. With the help of a differentiable physics simulator, we requireonly a single real-world demonstration. Empirical validations on both simulatedand real-world rope manipulation setups clearly show that our method canmanipulate different ropes with a single demonstration and significantlyoutperforms the baseline in both environments (62% improvement in in-domainropes, and 15% improvement in out-of-distribution ropes in simulation, 26%improvement in real-world), demonstrating the effectiveness of our approach inone-shot rope manipulation.

GenORM: Generalizable One-shot Rope Manipulation with Parameter-Aware Policy