?ETRobot Maid Cleans Up After 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 programmed to scan a messy room， identify 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， researchers say.

Previously scientists had developed robots that can grasp objects， but when it came to putting them back down again， the machines could place only single items down on flat surfaces. Now researchers are developing 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 arm， surveys objects in rooms by using a Microsoft 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 appropriate 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 if 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 adapt 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 detailed 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.

自動翻譯僅供參考

機器人女仆能夠幫助清理殘局 Robot女仆清理后你

機器人在冰箱中放置一個項目。

機器人可能很快發揮女傭和管家的家庭，有一個機器人，現在編程掃描凌亂的房間，發現所有的項目，找出屬于他們的地方，并把它們放回原處。

這樣的機器人還可以幫助包裝倉庫，清理汽車修理店，研究人員說。

此前科學家已經開發機器人，可以抓住物體，但是當它來重新把它們背下來，該機器可以向下放置在平面上只單品。現在，研究人員正在開發的機器，可以探測一組東西中，并放置在復雜的三維空間。

［哪里是我的機器人女仆？Where‘s My Robot Maid？微軟Kinect攝像頭，配備了一個紅外掃描儀，以幫助創建項目的3D模型。 Kinect的最初是為視頻游戲，但正在被廣泛使用的機器人專家來幫助機器人導航室。

Droid的交織在一起的許多圖像來創建一個房間的全貌。然后，它把這個觀點成為這取決于它們的顏色和形狀的塊。該機然后計算怎么可能它看到任何塊是一個給定的對象。然后，它決定在適當的家為項，創建目標空間的3D模型，并將該對象在該地方，考慮到兩者的項目和一個穩定放置。

的空間內的形狀（前各種物品，如書籍的運動，機器人所示的例子，來學習他們可能有共同的哪些特點的機器人也顯示了在那里事先放置物品的一些例子，并從中學習有類似的對象可能或，可能不會去，如明知不可把鞋子放在冰箱里）

研究人員的機器人收拾餐具，書籍，蛋盒，玩具，服裝等物品＆mdash; 98物體在所有的＆mdash;通過將它們在40個地區，如書架，菜架，冰箱，衣柜和桌子上。

機器人證明高達98％的成功識別并正確地收拾它。

u0026 以前見過的對象，你怎么能這樣可能想像，如果一個機器人既沒有看到一個馬提尼酒杯，也沒有之前的高腳杯持有人，這將是能夠把它扔掉 ？;研究人員說，Ashutosh說Saxena先生，一個機器人專家在康奈爾大學。 我們發現，它把它扔掉成功＆mdash;一個硬任務來完成 。

學到穩定的常識性的物理學原理， Saxena先生告訴InnovationNewsDaily。 從數據中學習這些基本原則，允許它來處理，并適應新的形勢和 ;

美國人愿意支付洗衣，折疊機器人Americans Willing to Pay for Laundry-Folding Robots成功率下降到平均82％。對象是最經常誤了曖昧的形狀＆mdash;例如，衣服和鞋子。此外， 感知一個啤酒瓶是否滿或空是很難的，因此它從來沒有完全想通了，做什么用啤酒瓶＆mdash;它只是拋出所有的人都變成了回收站，空或滿，就目前而言， Saxena先生說。

世界上已經有吸塵器機器人，擁有超過800萬Roombas銷售，并與 ;很快，我覺得兩到四年，我們將看到更強大的機器人＆mdash;例如，一個2英尺高的機器人用小臂，不僅吸塵地板上，而且拾取并放置東西的一側， Saxena先生說。他指出，他的團隊很快就會有這樣的移動機器人，他們可以用自己的算法編程。

但是， 這項工作是邁向清潔和房子安排機器人， 只是第一步; Saxena先生說。 需要大量的工作要做在此之前的機器人可能是有用的。你會很高興，如果它打破了五分之一的眼鏡？什么號大約每50？有可能。打破只有5000人會真正真棒。然而，這需要大量的從1到去50，我們現在的情況，僅1 5000突破和 ;

研究人員希望改善與更高分辨率的攝像頭的機器人。在機器人的手觸覺傳感器也可以幫助它知道一個對象是否處于穩定的位置，并且可以釋放。

該機還可以進行編程，以了解哪些對象應該屬于＆mdash的偏好;例如，電視遙控器理想是去旁邊的沙發在電視。

Saxena先生和他的同事在五月發行的機器人，國際在線雜志詳細介紹了他們的發現對前