机器人铺床织物拾取点的深度迁移学习
Deep Transfer Learning of Pick Points on Fabric for Robot Bed-Making
摘要：
服装折叠和纺织品制造中操纵织物的一个基本挑战是计算“拾取点”以有效地修改不确定流形的状态。我们提出了一种有监督的深度迁移学习方法来使用深度图像定位拾取点，以实现颜色和纹理的不变性。我们考虑铺床的任务，其中机器人依次抓住并拉动拾取点以增加毯子覆盖范围。我们进行物理实验，两个移动机械手机器人，丰田HSR和取，和三个不同颜色和纹理的毯子。我们比较了（1）人类监督，（2）在最上面的毯子点拾取的基线，以及（3）学习拾取点的覆盖结果。在四分之一比例的双床上，使用来自两个机器人的组合数据训练的模型实现了92%的毯子覆盖率，而基线为83%，人类监督员为95%。该模型转移到两个新的毯子，并达到93%的覆盖率。193张床位的平均覆盖率结果为92%，表明可以有效地学习织物上的转移不变机器人拾取点。
英文摘要：
A fundamental challenge in manipulating fabric for clothes folding and textiles manufacturing is computing "pick points" to effectively modify the state of an uncertain manifold. We present a supervised deep transfer learning approach to locate pick points using depth images for invariance to color and texture. We consider the task of bed-making, where a robot sequentially grasps and pulls at pick points to increase blanket coverage. We perform physical experiments with two mobile manipulator robots, the Toyota HSR and the Fetch, and three blankets of different colors and textures. We compare coverage results from (1) human supervision, (2) a baseline of picking at the uppermost blanket point, and (3) learned pick points. On a quarter-scale twin bed, a model trained with combined data from the two robots achieves 92% blanket coverage compared with 83% for the baseline and 95% for human supervisors. The model transfers to two novel blankets and achieves 93% coverage. Average coverage results of 92% for 193 beds suggest that transfer-invariant robot pick points on fabric can be effectively learned.
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