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Baxter练习抓取技能

Grabbing a pen or pair of sunglasses might be effortless for you or me, but it’s fiendishly difficult for a robot, especially if the object in question is unfamiliar or positioned awkwardly.
对于你我来说,抓起一支笔或拿起太阳镜是举手之劳,但它对于一个机器人来说却是极其困难的技术活,尤其是要拿的东西是机器人不熟悉的或者位置是蹊跷旮旯的。
Practice makes perfect, though, as one robot is proving. It is teaching itself to grasp all sorts of objects through hours of repetition. The robot uses different cameras and infrared sensors to look at an unfamiliar object from various angles before attempting to pick it up. Then it does so using several different grasps, shaking the object to make sure it is held securely. It may take dozens of tries for the robot to find the right grasp, and dozens more for it to make sure an object won’t slip.
不过机器人也同样证明一个道理:熟能生巧。通过几个小时的重复,机器人教会自己get了各种各样的抓取新技能。机器人使用装备的各种摄像机和红外传感器,从不同的角度观察陌生的对象,然后再尝试选择它。然后,它使用几种不同的抓取技巧,抓住的时候还轻轻摇晃几下,以确保自己真的抓牢了。这可能需要机器人进行几十次尝试才能get这项技能,然后再尝试几十次,才能确保抓取的东西不会滑落。
That might seem like a tedious process, but once the robot has learned how to pick something up, it can share that knowledge with other robots that have the same sensors and grippers. The researchers behind the effort eventually hope to have hundreds of robots learn collectively how to grasp a million different things.
这似乎是一个繁琐的过程,但一旦机器人学会了捡东西,它就能把抓取技能共享给予与它具有相同传感器和手爪的其他机器人。研究人员希望有上百个机器人共同学习如何掌握一百万种不同的东西。
The work was done by Stefanie Tellex, an assistant professor at Brown University, together with one of her graduate students, John Oberlin. They used a two-armed industrial robot called Baxter, made by the Boston-based company Rethink Robotics.
这项研究是由布朗大学的助理教授Stefanie Tellex和她的研究生John Oberlin一起进行的。他们用的机器人是由位于波士顿的机器人公司Rethink Robotics研制的双臂工业机器人——Baxter。
At the Northeast Robotics Colloquium, an event held at Worcester Polytechnic Institute this month, Oberlin demonstrated the robot’s gripping abilities to members of the public.
本月,在伍斯特理工学院举行的东北机器人学术讨论会上,Oberlin 向与会人员展示了这款机器人的抓取能力。
Enabling robots to manipulate objects more easily is one of the big challenges in robotics today, and it could have major industrial significance (see “Shelf-Picking Robots Will Vie for Amazon Prize”).
使机器人操纵工具更容易是当今机器人技术的一个巨大挑战,也可能有重大的工业意义(类似的如“货架分拣机器人将争夺亚马逊奖”)。
Tellex says robotics researchers are increasingly looking for more efficient ways of training robots to perform tasks such as manipulation. “We have powerful algorithms now—such as deep learning—that can learn from large data sets, but these algorithms require data,” she says. “Robot practice is a way to acquire the data that a robot needs for learning to robustly manipulate objects.”
Tellex 说机器人研究者越来越多地寻找更有效的方式训练机器人执行任务等操作。她说:“我们有强大的算法,如深度学习,可以从大数据集学习,但这些算法需要数据,”她说。“机器人从事的练习就是通过获取数据来学习操控物体。”
Tellex also notes that there are around 300 Baxter robots in various research labs around the world today. If each of those robots were to use both arms to examine new objects, she says, it would be possible for them to learn to grasp a million objects in 11 days. “By having robots share what they’ve learned, it’s possible to increase the speed of data collection by orders of magnitude,” she says.
Tellex 指出,目前在世界各地的各种研究实验室,有300个左右的Baxter机器人。如果每个人都使用两个手臂来抓取新的物体,她说,他们有可能在11天内学会掌握一百万个物体。她说:“通过让机器人分享他们所学到的东西,可以提高数据采集的速度”。
To grasp each object, the Brown researchers’ robot scans it from various angles using one of the cameras in its arms and the infrared sensors on its body. This allows it to identify possible locations at which to grasp. The researchers used a mathematical technique to optimize the process of practicing different grips. With this technique, the team’s Baxter robot picked up objects as much as 75 percent more reliably than it did using its regular software. The information acquired for each object—the images, the 3-D scans, and the correct grip—is encoded in a format that allows it to be shared online.
为了抓住每一个物体,布朗大学研究者们用Baxter的手臂和红外线传感器,从不同角度对它进行扫描。这使得它能够确定抓取的位置。研究者使用了一种数学方法来优化不同的夹具的加工过程。利用这种技术,团队的Baxter机器人抓取物体的可靠程度比使用常规的软件可靠程度高出75%。每个对象的信息获取的图像,三维扫描,和正确的抓地力是编码的格式,都能共享在线。
Other groups are developing methods to allow robots to learn to perform various tasks, including grasping. One of the most promising ways to achieve this is deep learning using so-called neural networks, which are simulations loosely modeled on the way nerves in the brain process information and learn (see “Robot Toddler Learns to Stand by ‘Imagining’ How to Do It”).
其他小组也正在开发如何让机器人学习执行各种任务,包括抓取。最有前途的方法之一,是深度学习使用所谓的神经网络,这是模拟松散的神经在大脑中的过程信息和学习的方式神经(见“机器人蹒跚学步 通过观察学习 学会了站立)。
Although humans acquire an ability to grasp through learning, a child doesn’t need to spend so much time handling different objects, and he or she can use previous experience to figure out very quickly how to pick up a new object. Tellex says the ultimate goal of her project is to give robots similar abilities. “Our long-term aim is to use this data to generalize to novel objects,” she says.
虽然人类通过学习掌握了抓取能力,但孩子不需要花那么多时间来对付不同的物体,他或她可以用以前的经验来迅速抓取新的物体。Tellex说她的项目的最终目标是给机器人类似的能力。她说:“我们的长期目标是利用这个数据来概括新的对象。

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