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.

本文提出了一种名为 GenORM 的框架，该框架可以通过单个真实演示来处理多种可变形绳索的操作策略，同时借助不可微分物理模拟器来实现，提高了一次性绳索操纵的效率和性能。

具有参数感知策略的通用单次绳索操作

Generalizable One-shot Rope Manipulation with Parameter-Aware Policy

In recent years, deep generative models have been shown to 'imagine'
convincing high-dimensional observations such as images, audio, and even video,
learning directly from raw data. In this work, we ask how to imagine
goal-directed visual plans -- a plausible sequence of observations that
transition a dynamical system from its current configuration to a desired goal
state, which can later be used as a reference trajectory for control. We focus
on systems with high-dimensional observations, such as images, and propose an
approach that naturally combines representation learning and planning. Our
framework learns a generative model of sequential observations, where the
generative process is induced by a transition in a low-dimensional planning
model, and an additional noise. By maximizing the mutual information between
the generated observations and the transition in the planning model, we obtain
a low-dimensional representation that best explains the causal nature of the
data. We structure the planning model to be compatible with efficient planning
algorithms, and we propose several such models based on either discrete or
continuous states. Finally, to generate a visual plan, we project the current
and goal observations onto their respective states in the planning model, plan
a trajectory, and then use the generative model to transform the trajectory to
a sequence of observations. We demonstrate our method on imagining plausible
visual plans of rope manipulation.

该论文探讨了如何利用深度生成模型和计划算法生成高维图像的目标导向视觉规划序列，并以绳索操纵为例进行了演示。