Object rearranging is one of the most common deformable manipulation tasks,
where the robot needs to rearrange a deformable object into a goal
configuration. Previous studies focus on designing an expert system for each
specific task by model-based or data-driven approaches and the application
scenarios are therefore limited. Some research has been attempting to design a
general framework to obtain more advanced manipulation capabilities for
deformable rearranging tasks, with lots of progress achieved in simulation.
However, transferring from simulation to reality is difficult due to the
limitation of the end-to-end CNN architecture. To address these challenges, we
design a local GNN (Graph Neural Network) based learning method, which utilizes
two representation graphs to encode keypoints detected from images.
Self-attention is applied for graph updating and cross-attention is applied for
generating manipulation actions. Extensive experiments have been conducted to
demonstrate that our framework is effective in multiple 1-D (rope, rope ring)
and 2-D (cloth) rearranging tasks in simulation and can be easily transferred
to a real robot by fine-tuning a keypoint detector.

使用基于 GNN 的学习方法，通过两个表示图对图像中检测到的关键点进行编码。采用自注意力进行图更新和交叉注意力进行生成操作，实验表明该框架在多个 1-D 和 2-D 重排任务中非常有效，并且可以通过微调关键点检测器轻松转移到真实机器人中。

基于本地 GNN 的深度强化学习用于目标驱动的变形物体重新排列

Deep Reinforcement Learning Based on Local GNN for Goal-conditioned Deformable Object Rearranging

We present a novel Learning from Demonstration (LfD) method, Deformable
Manipulation from Demonstrations (DMfD), to solve deformable manipulation tasks
using states or images as inputs, given expert demonstrations. Our method uses
demonstrations in three different ways, and balances the trade-off between
exploring the environment online and using guidance from experts to explore
high dimensional spaces effectively. We test DMfD on a set of representative
manipulation tasks for a 1-dimensional rope and a 2-dimensional cloth from the
SoftGym suite of tasks, each with state and image observations. Our method
exceeds baseline performance by up to 12.9% for state-based tasks and up to
33.44% on image-based tasks, with comparable or better robustness to
randomness. Additionally, we create two challenging environments for folding a
2D cloth using image-based observations, and set a performance benchmark for
them. We deploy DMfD on a real robot with a minimal loss in normalized
performance during real-world execution compared to simulation (~6%). Source
code is on this http URL

本论文提出了一种新颖的基于演示的学习方法 —— 来自演示的可变形操作（DMfD），用于解决可变形操纵任务，它能够有效地处理高维空间和图像观测，并在 SoftGym 组件上取得了较高的性能。同时，本文还在两个具有挑战性的环境中进行了折叠布料任务的基准测试，并将 DMfD 运用于真实机器人中，与仿真损失相比性能损失最小。