高潮一区二区三区乱码,久久中文字幕国产

機(jī)器人女仆幫你清理殘局

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(dòng)翻譯僅供參考機(jī)器人女仆能夠幫助清理殘局 Robot女仆清理后你機(jī)器人在冰箱中放置一個(gè)項(xiàng)目。機(jī)器人可能很快發(fā)揮女傭和管家的家庭，有一個(gè)機(jī)器人，現(xiàn)在編程掃描凌亂的房間，發(fā)現(xiàn)所有的項(xiàng)目，找出屬于他們的地方，并把它們放回原處。這樣的機(jī)器人還可以幫助包裝倉(cāng)庫(kù)，清理汽車(chē)修理店，研究人員說(shuō)。此前科學(xué)家已經(jīng)開(kāi)發(fā)機(jī)器人，可以抓住物體，但是當(dāng)它來(lái)重新把它們背下來(lái)，該機(jī)器可以向下放置在平面上只單品?，F(xiàn)在，研究人員正在開(kāi)發(fā)的機(jī)器，可以探測(cè)一組東西中，并放置在復(fù)雜的三維空間。［哪里是我的機(jī)器人女仆？Where‘s My Robot Maid？微軟Kinect攝像頭，配備了一個(gè)紅外掃描儀，以幫助創(chuàng)建項(xiàng)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(jī)器人專(zhuān)家來(lái)幫助機(jī)器人導(dǎo)航室。 Droid的交織在一起的許多圖像來(lái)創(chuàng)建一個(gè)房間的全貌。然后，它把這個(gè)觀點(diǎn)成為這取決于它們的顏色和形狀的塊。該機(jī)然后計(jì)算怎么可能它看到任何塊是一個(gè)給定的對(duì)象。然后，它決定在適當(dāng)?shù)募覟轫?xiàng)，創(chuàng)建目標(biāo)空間的3D模型，并將該對(duì)象在該地方，考慮到兩者的項(xiàng)目和一個(gè)穩(wěn)定放置。的空間內(nèi)的形狀（前各種物品，如書(shū)籍的運(yùn)動(dòng)，機(jī)器人所示的例子，來(lái)學(xué)習(xí)他們可能有共同的哪些特點(diǎn)的機(jī)器人也顯示了在那里事先放置物品的一些例子，并從中學(xué)習(xí)有類(lèi)似的對(duì)象可能或，可能不會(huì)去，如明知不可把鞋子放在冰箱里）研究人員的機(jī)器人收拾餐具，書(shū)籍，蛋盒，玩具，服裝等物品＆mdash; 98物體在所有的＆mdash;通過(guò)將它們?cè)?0個(gè)地區(qū)，如書(shū)架，菜架，冰箱，衣柜和桌子上。機(jī)器人證明高達(dá)98％的成功識(shí)別并正確地收拾它。 u0026 以前見(jiàn)過(guò)的對(duì)象，你怎么能這樣可能想像，如果一個(gè)機(jī)器人既沒(méi)有看到一個(gè)馬提尼酒杯，也沒(méi)有之前的高腳杯持有人，這將是能夠把它扔掉？;研究人員說(shuō)，Ashutosh說(shuō)Saxena先生，一個(gè)機(jī)器人專(zhuān)家在康奈爾大學(xué)。我們發(fā)現(xiàn)，它把它扔掉成功＆mdash;一個(gè)硬任務(wù)來(lái)完成。學(xué)到穩(wěn)定的常識(shí)性的物理學(xué)原理， Saxena先生告訴InnovationNewsDaily。從數(shù)據(jù)中學(xué)習(xí)這些基本原則，允許它來(lái)處理，并適應(yīng)新的形勢(shì)和 ; 美國(guó)人愿意支付洗衣，折疊機(jī)器人Americans Willing to Pay for Laundry-Folding Robots成功率下降到平均82％。對(duì)象是最經(jīng)常誤了曖昧的形狀＆mdash;例如，衣服和鞋子。此外，感知一個(gè)啤酒瓶是否滿(mǎn)或空是很難的，因此它從來(lái)沒(méi)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(mǎn)，就目前而言， Saxena先生說(shuō)。世界上已經(jīng)有吸塵器機(jī)器人，擁有超過(guò)800萬(wàn)Roombas銷(xiāo)售，并與 ;很快，我覺(jué)得兩到四年，我們將看到更強(qiáng)大的機(jī)器人＆mdash;例如，一個(gè)2英尺高的機(jī)器人用小臂，不僅吸塵地板上，而且拾取并放置東西的一側(cè)， Saxena先生說(shuō)。他指出，他的團(tuán)隊(duì)很快就會(huì)有這樣的移動(dòng)機(jī)器人，他們可以用自己的算法編程。但是，這項(xiàng)工作是邁向清潔和房子安排機(jī)器人，只是第一步; Saxena先生說(shuō)。需要大量的工作要做在此之前的機(jī)器人可能是有用的。你會(huì)很高興，如果它打破了五分之一的眼鏡？什么號(hào)大約每50？有可能。打破只有5000人會(huì)真正真棒。然而，這需要大量的從1到去50，我們現(xiàn)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(jī)器人。在機(jī)器人的手觸覺(jué)傳感器也可以幫助它知道一個(gè)對(duì)象是否處于穩(wěn)定的位置，并且可以釋放。該機(jī)還可以進(jìn)行編程，以了解哪些對(duì)象應(yīng)該屬于＆mdash的偏好;例如，電視遙控器理想是去旁邊的沙發(fā)在電視。 Saxena先生和他的同事在五月發(fā)行的機(jī)器人，國(guó)際在線雜志詳細(xì)介紹了他們的發(fā)現(xiàn)對(duì)前

標(biāo)簽：郴州黔東廈門(mén) 延安保山紅河韶關(guān) 濟(jì)源

巨人網(wǎng)絡(luò)通訊聲明：本文標(biāo)題《機(jī)器人女仆幫你清理殘局》，本文關(guān)鍵詞機(jī)器人,女仆,幫你,清理,；如發(fā)現(xiàn)本文內(nèi)容存在版權(quán)問(wèn)題，煩請(qǐng)?zhí)峁┫嚓P(guān)信息告之我們，我們將及時(shí)溝通與處理。本站內(nèi)容系統(tǒng)采集于網(wǎng)絡(luò)，涉及言論、版權(quán)與本站無(wú)關(guān)。