论文标题
学习任意目标的织物折叠，并使用一个小时的真实机器人体验
Learning Arbitrary-Goal Fabric Folding with One Hour of Real Robot Experience
论文作者
论文摘要
在机器人技术中，操纵可变形物体（例如织物）是一个长期存在的问题，国家估计和控制对传统方法构成了重大挑战。在本文中，我们表明，只有一个小时的自我监督的真实机器人体验，就可以在没有人类的监督或模拟的情况下学习面料折叠技能。我们的方法依赖于完全卷积的网络和操纵视觉输入来利用学习的功能，从而使我们能够创建一个表现力的目标条件的选秀权和放置策略，只能使用现实世界的机器人数据进行有效培训。仅通过稀疏的奖励功能学习折叠技能，因此不需要奖励功能工程，仅仅是目标配置的图像。我们在一组毛巾折叠任务上演示了我们的方法，并表明我们的方法能够发现顺序折叠策略纯粹是从反复试验中发现的。我们实现了最新的结果，而无需在先前的方法中使用演示或模拟。可用的视频，网址为：https：//sites.google.com/view/learningtofold
Manipulating deformable objects, such as fabric, is a long standing problem in robotics, with state estimation and control posing a significant challenge for traditional methods. In this paper, we show that it is possible to learn fabric folding skills in only an hour of self-supervised real robot experience, without human supervision or simulation. Our approach relies on fully convolutional networks and the manipulation of visual inputs to exploit learned features, allowing us to create an expressive goal-conditioned pick and place policy that can be trained efficiently with real world robot data only. Folding skills are learned with only a sparse reward function and thus do not require reward function engineering, merely an image of the goal configuration. We demonstrate our method on a set of towel-folding tasks, and show that our approach is able to discover sequential folding strategies, purely from trial-and-error. We achieve state-of-the-art results without the need for demonstrations or simulation, used in prior approaches. Videos available at: https://sites.google.com/view/learningtofold
