论文标题
多步，多任务面料操纵的视觉空间远见
VisuoSpatial Foresight for Multi-Step, Multi-Task Fabric Manipulation
论文作者
论文摘要
机器人织物操纵在家庭机器人技术，纺织品，高级护理和手术中都有应用。但是，现有的面料操纵技术是为特定任务而设计的，因此很难跨越不同但相关的任务。我们将视觉前瞻性框架扩展到学习面料动力学，可以有效地重复使用，以通过单个目标条件策略完成不同的面料操纵任务。我们介绍了视觉空间的前瞻性（VSF），它通过在域随机RGB图像上学习视觉动态和深度映射在先前的工作基础上，同时且完全在模拟中。我们通过在模拟和DA Vinci Research Kit（DVRK）手术机器人中的5种基线方法和折叠任务进行实验评估VSF，而在火车或测试时间没有任何演示的情况下，对5种基线方法进行了评估。此外，我们发现利用深度显着提高了性能。 RGBD数据比纯RGB数据的织物折叠成功率提高了80％。代码，数据，视频和补充材料可在https://sites.google.com/view/fabric-vsf/上找到。
Robotic fabric manipulation has applications in home robotics, textiles, senior care and surgery. Existing fabric manipulation techniques, however, are designed for specific tasks, making it difficult to generalize across different but related tasks. We extend the Visual Foresight framework to learn fabric dynamics that can be efficiently reused to accomplish different fabric manipulation tasks with a single goal-conditioned policy. We introduce VisuoSpatial Foresight (VSF), which builds on prior work by learning visual dynamics on domain randomized RGB images and depth maps simultaneously and completely in simulation. We experimentally evaluate VSF on multi-step fabric smoothing and folding tasks against 5 baseline methods in simulation and on the da Vinci Research Kit (dVRK) surgical robot without any demonstrations at train or test time. Furthermore, we find that leveraging depth significantly improves performance. RGBD data yields an 80% improvement in fabric folding success rate over pure RGB data. Code, data, videos, and supplementary material are available at https://sites.google.com/view/fabric-vsf/.
