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.

翻译：由于运动过程中形变不确定性的固有特性，以往的绳索操控方法通常需要数百次真实世界演示才能为每根绳索训练操控策略，即使对于绳索目标到达这样的简单任务也是如此，这阻碍了其在瞬息万变的世界中的应用。为解决这一问题，我们提出了GenORM框架，该框架允许操控策略仅通过单次真实世界演示即可处理不同可变形绳索。为实现这一目标，我们通过将策略条件化于可变形绳索参数，并使用多种模拟可变形绳索进行训练，使策略能够根据不同绳索参数调整动作。在推理时，给定新绳索，GenORM通过最小化真实世界演示点云网格密度与仿真结果之间的差异来估计可变形绳索参数。借助可微物理仿真器，我们仅需单次真实世界演示。在模拟和真实世界绳索操控装置上的实证验证清晰表明，我们的方法可通过单次演示操控不同绳索，且在两种环境中均显著优于基线（模拟中域内绳索提升62%，分布外绳索提升15%，真实世界中提升26%），证明了该方法在单次绳索操控中的有效性。