学习使用目标条件Transporter网络重新排列可变形电缆、织物和袋子
Learning to Rearrange Deformable Cables, Fabrics, and Bags with Goal-Conditioned Transporter Networks
AI总结：
本研究提出将目标条件嵌入Transporter网络，用于可变形物体（电缆、织物、袋子）的重新排列操作，通过模拟和物理实验验证了其有效性，无需测试时视觉锚点。
摘要：
重新排列和操纵可变形物体（如电缆、织物和袋子）是机器人操作中一个长期存在的挑战。与刚性物体相比，可变形物体的复杂动力学和高维配置空间使得操作不仅在多步规划上困难，甚至在目标规范上也很困难。目标不能像刚性物体姿态那样容易指定，可能涉及复杂的相对空间关系，如“将物品放入袋子内”。在这项工作中，我们开发了一套包含1D、2D和3D可变形结构的模拟基准，包括涉及基于图像的目标条件和多步可变形操作的任务。我们提出将目标条件嵌入到Transporter网络中，这是一种最近提出的用于学习机器人操作的模型架构，通过重新排列深度特征来推断可以表示拾取和放置动作的位移。在模拟和物理实验中，我们证明了目标条件的Transporter网络使智能体能够将可变形结构操纵到灵活指定的配置中，而无需测试时的目标位置视觉锚点。我们还通过测试2D和3D可变形任务，显著扩展了先前使用Transporter网络操纵可变形物体的结果。补充材料可在https://berkeleyautomation.github.io/bags/获取。
英文摘要：
Rearranging and manipulating deformable objects such as cables, fabrics, and bags is a long-standing challenge in robotic manipulation. The complex dynamics and high-dimensional configuration spaces of deformables, compared to rigid objects, make manipulation difficult not only for multi-step planning, but even for goal specification. Goals cannot be as easily specified as rigid object poses, and may involve complex relative spatial relations such as "place the item inside the bag". In this work, we develop a suite of simulated benchmarks with 1D, 2D, and 3D deformable structures, including tasks that involve image-based goal-conditioning and multi-step deformable manipulation. We propose embedding goal-conditioning into Transporter Networks, a recently proposed model architecture for learning robotic manipulation that rearranges deep features to infer displacements that can represent pick and place actions. In simulation and in physical experiments, we demonstrate that goal-conditioned Transporter Networks enable agents to manipulate deformable structures into flexibly specified configurations without test-time visual anchors for target locations. We also significantly extend prior results using Transporter Networks for manipulating deformable objects by testing on tasks with 2D and 3D deformables. Supplementary material is available at https://berkeleyautomation.github.io/bags/.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
