‘Avoid cat’, or ‘torment cat’?

This article was originally published at The Conversation. The publication contributed the article to Live Science's Expert Voices: Op-Ed & Insights.

James Dyson’s decision to fund a robotics laboratory at Imperial College London may not lead to the super advanced robot friends of our dreams, but what he has planned could make robotic domestic appliances significantly more realistic.

The Dyson Robotics Laboratory is a ￡5 million collaboration between the British-based domestic technology company and one of the UK’s leading computer science departments. Researchers at the centre will investigate how to help robots not only sense their surroundings but be able to identify objects within their immediate environment. This, it is hoped, will mean a robot could sort out your dirty washing or clear a table.

It seems everyone is getting in on the robotics act lately. The UK government has highlighted the field among its eight top priorities for science spending and the European Commission has pledged new funding for robotics research.

Google has bought both Boston Dynamics, famous for its animal-like robots, and artificial intelligence specialists DeepMind. Amazon, meanwhile, is teasing us with promises of deliveries-by-drone in the future.

Dyson’s comments about creating “machines that see and think in the way that we do” have inevitably led to excitement about futuristic robot servants. We should try to contain our excitement on this front as we are likely to be disappointed. It is highly unlikely the Dyson lab or any of the other bold plans announced over the past 12 months will lead to robots capable of learning or acting like humans. Most robots, even the most sophisticated, are still usually only capable of doing one thing. The difficulties of robotic perception, cognition and action in the real world are such that the required general purpose intelligence is still many generations away.

But this new centre is not about general purpose intelligence. It is focusing on 3D sensing, a fairly well understood robotics technology that could realistically produce applications in the near term future.

From 2D to 3D

Most tasks a robot must do, say, grasping objects or driving through crowds, involve measuring. For many years robots could only measure in 2D, typically using sonar or laser. The jump to 3D is essential, and has been made possible by devices such as the Microsoft Kinect and also by monocular visual SLAM, a technique pioneered by Andrew Davidson, the very man who will lead the new Dyson lab.

“Monocular” means this technique uses just one camera, while “visual” means the camera uses normal, visible light, unlike the Kinect, which uses infrared. This is important as normal light captures useful information about the world, such as colour, which is useful for telling objects apart, and shadows that can help indicate shape and position, neither of which are visible in other bandwidths.

The “SLAM” part stands for simultaneous localisation and mapping. This is the technique of building a map – a 3D picture of the world – while working out where you are on that map at the same time. When a robot moves around a room, visual SLAM is performed by finding distinctive parts of an image, then tracking how these parts move as the robot moves.

SLAM is an essential technology in almost all robots that move around, as it allows them to work out where they are. A 3D map can also allow a robot to find objects in its environment, such as your keys, or avoid obstacles, such as your cat. It can also use the map to decide how best to do things, such as hoovering your floor using the shortest route possible.

Cameras are an ideal sensor for a home robot because they can be small, light and cheap. This means future Dyson home robots will be able to map your home in 3D for very little extra cost. Images from cameras can also be used for object and face recognition, which would make robotic home help even more efficient.

The Dyson lab won’t bring us Rosie the Robot Maid any time soon but this investment could open the way for a new generation of single-purpose intelligent domestic appliances. It could bring us the robot vacuum that can clean around your complicated media centre and perhaps even something that can tidy up a child’s bedroom without putting everything in the wrong place. That’s a pretty enticing prospect for most parents.

Nick Hawes researches and teaches robotics at the University of Birmingham. He receives funding from both EPSRC and the European Commission to research robotics technology and its application to a range of domains including security and healthcare.

This article was originally published on The Conversation. Read the original article. The views expressed are those of the author and do not necessarily reflect the views of the publisher. This version of the article was originally published on Live Science.

您期望Dyson的新机器人实验室能够做些什么？

“避免猫'，或'折磨猫”？

詹姆斯·戴森的;决定资助在英国伦敦帝国学院的机器人实验室可能不会导致我们的梦想的超级先进的机器人朋友，但他已计划可以让机器人家用电器显著更加逼真.

戴森机器人实验室是一个＆磅; 500万英国的国内技术公司和英国的一个之间的合作;领先的计算机科学系。研究人员在该中心将研究如何帮助机器人不仅感知周围的环境，但能在他们身边的环境识别物体。这一点，我们希望，这将意味着一个机器人可以理清你的脏衣物或清除表.

似乎每个人都在机器人获得的最近行动。英国政府强调了该领域中的八个最优先的科学投入和欧盟委员会已经承诺为机器人研究.

谷歌已经收购了两个波士顿动力公司，著名的动物般的机器人和人工智能专家DeepMind新的资金。亚马逊，同时，被戏弄我们交付逐无人驾驶的.

戴森的未来的承诺;言论有关创建和;那看到和思考，我们路机等;不可避免地导致兴奋未来的机器人仆人。我们应该试图遏制内心的兴奋在这方面，我们很可能会失望。这是极不可能的戴森实验室或任何宣布，在过去12个月，其他大胆的计划将导致能够学习或表现得象人类的机器人。大多数的机器人，即使是最复杂的，仍然是通常只能够做一件事。机器人感知，认知和行为在现实世界中的困难，是这样的，所需要的通用智力仍然是许多代远.

但是这个新的中心是不是通用的智力。它的重点是3D感应，一个相当容易理解的机器人技术，可以产生现实在短期内未来的应用。

从2D到3D

大部分任务的机器人必须做的，比方说，抓物或驾车穿越人群，涉及测量。多年来，机器人只能测量2D，通常使用声纳或激光。跳转到3D是必不可少的，并已成为可能，如微软Kinect也由单眼视觉SLAM，由安德鲁·戴维森，非常人谁将会引领新的戴森实验室.

u0026首创的技术设备;单眼;的意味着这种技术只使用一个摄像头，而;视觉;的是指相机使用正常的，可见光，不同的是Kinect的，它使用红外线。这是重要的，因为普通光捕获关于世界的有用信息，如颜色，这是告诉对象分开是有用的，和阴影，可以帮助指示的形状和位置，这两者都不是可见的其他带宽.

该 ;部分代表同步定位与地图。这是建设一个地图的的技术;世界的的3D画面;而工作了你在哪里上的地图在同一时间。当周围的房间的机器人的动作，视觉SLAM是通过寻找图像的独特部分，然后进行跟踪这些部分如何移动，机器人移动.

SLAM是在走动，因为它允许他们几乎所有的机器人的关键技术制定出他们在哪里。 3D地图还可以让机器人去发现它的环境，对象，比如你的钥匙，或者避开障碍物，比如你的猫。它也可以使用地图来决定如何最好地做事情，比如用最短的路线可能hoovering您的地板.

相机是家用机器人的理想的传感器，因为他们可以是小，重量轻，价格便宜。这意味着未来的戴森家庭机器人将能够到您家中的3D地图很少的额外费用。也可用于从摄像机的图像对象和面部识别，这将使机器人家庭帮助更有效.

戴森实验室荣获o的;吨给我们带来罗茜机器人女仆任何时间很快，但这种投资可能会为新开辟道路代单用途智能家用电器。它可以给我们带来的机器人真空，可以清理你的周围复杂的媒体中心，甚至的东西，可以整理一下孩子大局;卧室里没有把一切都错了地方。这个大局; SA相当诱人的前景，大多数家长.

尼克·霍斯研究和教导机器人在伯明翰大学。他获得资助来自EPSRC和欧洲委员会研究机器人技术及其应用到各种领域，包括安全和医疗.

这篇文章最初发表的谈话。阅读原创文章。表达的观点属于作者本人，并不一定反映出版者的看法。这个版的文章最初发表在现场科学。