2018年04月17日 | 机器人女仆帮你清理残局

 ETRobot Maid Cleans Up Afr Your Mess

A robot places an item in a refrigerator. Credit： Saxena Lab View full size image

Robots could soon play maid and butler in homes， with a droid now progmed to s a messy room， idenfy all items， figure out where they belong and put them back in place.

Such robots also could help pack warehouses and clean up auto repair shops， researche say.

Previously scienTIsts had developed robots that can grasp objects， but when it came to putTIng them back down agn， the machines could place only single items down on flat suaces. Now researchers are develong machines that can survey a group of things and place them in complex 3D spaces.

［Where‘s My Robot Maid？］

The robot， which has a single mechanical ， surveys objects in rooms by using a crosoft Kinect camera， which is equipped with an infrared scanner to help create 3D models of items. The Kinect was originally developed for video gaming but is being widely used by roboTIcists to help robots navigate rooms.

The droid weaves together many images to create an overall picture of a room. It then divides this view into blocks depending on their color and shape. The machine then computes how likely any block it sees is a given object. It then decides on an ropriate home for the item， creates a 3D model of the target space， and puts the object in that place， taking into account the shapes of both the item and the space for a stable placement.

（Before the exercise， the robot is shown examples of various kinds of items， such as books， to learn what characteristics they might have in common. The droid is also shown some examples of where to place objects beforehand， and from it learns where similar objects might or might not go， such as knowing not to put shoes in the refrigerator.）

The researchers’ robot tidied up dishes， books， egg cartons， toys， clothing and other items — 98 objects in all — by placing them in 40 areas， such as bookshelves， dish racks， refrigerators， closets and on tables.

The robot proved up to 98 percent successful in recognizing and correctly putting away objects it had seen before.

“How can you possibly imagine that a robot has neither seen a martini glass nor the stemware holder before， it would be able to put it away？” said researcher Ashutosh Saxena， a roboticist at Cornell University. “We show that it puts it away successfully — a hard task to do.”

“It learned the common-sense physics principles of stability，” Saxena told InnovationNewsDaily. “Learning these underlying principles from data allowed it to handle and apt to new situations.”

［Americans Willing to Pay for Laundry-Folding Robots］

The robot was also capable of placing objects it had never seen before， but success rates fell to an average of 82 percent. Objects that were most often misidentified had ambiguous shapes — for instance， clothing and shoes. In addition， “perceiving whether a beer bottle is full or empty is hard， and therefore it has never quite figured out what to do with beer bottles — it just throws all of them into the recycling bin， empty or full， for now，” Saxena said.

The world already has vacuum cleaner robots， with more than 8 million Roombas sold， and “very soon， I think two to four years， we‘ll see more capable robots — for example， a 2-foot-tall robot with a small arm that not only vacuums the floor， but also picks up and places things on the side，” Saxena said. He noted his team will soon have such mobile robots that they can program with their algorithms.

Still， “this work is only a first step towards a cleaning and house-arranging robot，” Saxena said. “A lot needs to be done before this robot could be useful. Would you be happy if it breaks one out of five glasses？ No. What about one in 50？ Maybe. Breaking only one in 5，000 would be really awesome. However， it takes a lot to go from 1 in 50， where we are now， to breaking only 1 in 5，000.”

The researchers hope to improve the robot with higher-resolution cameras. Tactile sensors in the droid’s hand also could help it know whether an object is in a stable position and can be released.

The machine also could be programmed to understand the preferences in which objects should belong — for instance， the TV remote control ideally would go next to the sofa in front of the TV.

Saxena and his colleagues detai their findings online in the May issue of the International Journal of Robotics.

This story was provided by InnovationNewsDaily， a sister site to LiveScience. Follow InnovationNewsDaily on Twitter @News_Innovation， or on Facebook.

自动翻译仅供参考

机器人女仆能够帮助清理残局 Robot女仆清理后你

机器人在冰箱中放置一个项目。

机器人可能很快发挥女佣和管家的家庭，有一个机器人，现在编程扫描凌乱的房间，发现所有的项目，找出属于他们的地方，并把它们放回原处。

这样的机器人还可以帮助仓库，清理汽车修理店，研究人员说。

此前科学家已经开发机器人，可以抓住物体，但是当它来重新把它们背下来，该机器可以向下放置在平面上只单品。现在，研究人员正在开发的机器，可以探测一组东西中，并放置在复杂的三维空间。

［哪里是我的机器人女仆？Where‘s My Robot Maid？微软Kinect摄像头，配备了一个扫描仪，以帮助创建项目的3D模型。 Kinect的最初是为视频游戏，但正在被广泛使用的机器人专家来帮助机器人导航室。

Droid的交织在一起的许多图像来创建一个房间的全貌。然后，它把这个观点成为这取决于它们的颜色和形状的块。该机然后计算怎么可能它看到任何块是一个给定的对象。然后，它决定在适当的家为项，创建目标空间的3D模型，并将该对象在该地方，考虑到两者的项目和一个稳定放置。

的空间内的形状（前各种物品，如书籍的运动，机器人所示的例子，来学习他们可能有共同的哪些特点的机器人也显示了在那里事先放置物品的一些例子，并从中学习有类似的对象可能或，可能不会去，如明知不可把鞋子放在冰箱里）

研究人员的机器人收拾餐具，书籍，蛋盒，玩具，服装等物品＆mdash; 98物体在所有的＆mdash;通过将它们在40个地区，如书架，菜架，冰箱，衣柜和桌子上。

机器人证明高达98％的成功识别并正确地收拾它。

u0026 以前见过的对象，你怎么能这样可能想像，如果一个机器人既没有看到一个马提尼酒杯，也没有之前的高脚杯持有人，这将是能够把它扔掉 ？;研究人员说，Ashutosh说Saxena先生，一个机器人专家在康奈尔大学。 我们发现，它把它扔掉成功＆mdash;一个硬任务来完成 。

学到稳定的常识性的物理学原理， Saxena先生告诉InnovationNewsDaily。 从数据中学习这些基本原则，允许它来处理，并适应新的形势和 ;

美国人愿意支付洗衣，折叠机器人Americans Willing to Pay for Laundry-Folding Robots成功率下降到平均82％。对象是最经常误了暧昧的形状＆mdash;例如，衣服和鞋子。此外， 感知一个啤酒瓶是否满或空是很难的，因此它从来没有完全想通了，做什么用啤酒瓶＆mdash;它只是抛出所有的人都变成了回收站，空或满，就目前而言， Saxena先生说。

世界上已经有吸尘器机器人，拥有超过800万Roombas销售，并与 ;很快，我觉得两到四年，我们将看到更强大的机器人＆mdash;例如，一个2英尺高的机器人用小臂，不仅吸尘地板上，而且拾取并放置东西的一侧， Saxena先生说。他指出，他的团队很快就会有这样的移动机器人，他们可以用自己的算法编程。

但是， 这项工作是迈向清洁和房子安排机器人， 只是第一步; Saxena先生说。 需要大量的工作要做在此之前的机器人可能是有用的。你会很高兴，如果它打破了五分之一的眼镜？什么号大约每50？有可能。打破只有5000人会真正真棒。然而，这需要大量的从1到去50，我们现在的情况，仅1 5000突破和 ;

研究人员希望改善与更高分辨率的摄像头的机器人。在机器人的手触觉也可以帮助它知道一个对象是否处于稳定的位置，并且可以释放。

该机还可以进行编程，以了解哪些对象应该属于＆mdash的偏好;例如，电视理想是去旁边的沙发在电视。

Saxena先生和他的同事在五月发行的机器人，国际在线杂志详细介绍了他们的发现对前