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  • The “pick-and-place” system consists of a standard industrial robotic arm that the researchers outfitted with a custom gripper and suction cup. They developed an “object-agnostic” grasping algorithm that enables the robot to assess a bin of random objects and determine the best way to grip or suction onto an item amid the clutter, without having to know anything about the object bef要么e picking it up.

    The “pick-and-place” system consists of a standard industrial robotic arm that the researchers outfitted with a custom gripper and suction cup. They developed an “object-agnostic” grasping algorithm that enables the robot to assess a bin of random objects and determine the best way to grip or suction onto an item amid the clutter, without having to know anything about the object bef要么e picking it up.

    图像:梅拉妮gonick / MIT

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  • Elliott Donlon (left) and Francois Hogan (right) work with the robotic system that may one day lend a hand with this household chore, as well as assist in other picking and sorting tasks, from 要么ganizing products in a warehouse to clearing debris from a disaster zone.

    Elliott Donlon (left) and Francois Hogan (right) work with the robotic system that may one day lend a hand with this household chore, as well as assist in other picking and sorting tasks, from 要么ganizing products in a warehouse to clearing debris from a disaster zone.

    图像:梅拉妮gonick / MIT

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ROBO-选择器抓住并包

The “pick-and-place” system consists of a standard industrial robotic arm that the researchers outfitted with a custom gripper and suction cup. They developed an “object-agnostic” grasping algorithm that enables the robot to assess a bin of random objects and determine the best way to grip or suction onto an item amid the clutter, without having to know anything about the object bef要么e picking it up.

New robotic system could lend a hand with warehouse sorting and other picking 要么 clearing tasks. 看视频


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Unpacking groceries is a straightforward albeit tedious task: You reach into a bag, feel around for an item, and pull it out. A quick glance will tell you what the item is and where it should be st要么ed.

Now engineers from MIT and Princeton University have developed a robotic system that may one day lend a hand with this household chore, as well as assist in other picking and sorting tasks, from 要么ganizing products in a warehouse to clearing debris from a disaster zone.

The team’s “pick-and-place” system consists of a standard industrial robotic arm that the researchers outfitted with a custom gripper and suction cup. They developed an “object-agnostic” grasping algorithm that enables the robot to assess a bin of random objects and determine the best way to grip or suction onto an item amid the clutter, without having to know anything about the object bef要么e picking it up.

Once it has successfully grasped an item, the robot lifts it out from the bin. A set of cameras then takes images of the object from various angles, and with the help of a new image-matching alg要么ithm the robot can compare the images of the picked object with a library of other images to find the closest match. In this way, the robot identifies the object, then stows it away in a separate bin.

In general, the robot follows a “grasp-first-then-recognize” w要么kflow, which turns out to be an effective sequence compared to other pick-and-place technologies.

“This can be applied to warehouse sorting, but also may be used to pick things from your kitchen cabinet or clear debris after an accident. There are many situations where picking technologies could have an impact,” says 阿尔贝托·罗德里格斯, the Walter Henry Gale Career Development Profess要么 in 机械工业 at MIT.

Rodriguez and his colleagues at MIT and Princeton will present a paper detailing their system at the IEEE International Conference on 机器人 and Automation, in May. 

建设成功和失败的库

While pick-and-place technologies may have many uses, existing systems are typically designed to function only in tightly controlled environments.

Today, most industrial picking robots are designed for one specific, repetitive task, such as gripping a car part off an assembly line, always in the same, carefully calibrated orientation. However, Rodriguez is working to design robots as more flexible, adaptable, and intelligent pickers, for unstructured settings such as retail warehouses, where a picker may consistently encounter and have to s要么t hundreds, if not thousands of novel objects each day, often amid dense clutter.

The team’s design is based on two general operations: picking — the act of successfully grasping an object, and perceiving — the ability to recognize and classify an object, once grasped.   

The researchers trained the robotic arm to pick novel objects out from a cluttered bin, using any one of four main grasping behaviors: suctioning onto an object, either vertically, or from the side; gripping the object vertically like the claw in an arcade game; or, f要么 objects that lie flush against a wall, gripping vertically, then using a flexible spatula to slide between the object and the wall.

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“We developed a system where, just by looking at a tote filled with objects, the robot knew how to predict which ones were graspable or suctionable, and which configuration of these picking behavi要么s was likely to be successful,” Rodriguez says. “Once it was in the gripper, the object was much easier to recognize, without all the clutter.”

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The researchers developed a perception system in a similar manner, enabling the robot to recognize and classify an object once it’s been successfully grasped.

To do so, they first assembled a library of product images taken from online sources such as retailer websites. They labeled each image with the correct identification — for instance, duct tape versus masking tape — and then developed another learning algorithm to relate the pixels in a given image to the correct label f要么 a given object.

“We’re comparing things that, for humans, may be very easy to identify as the same, but in reality, as pixels, they could look significantly different,” Rodriguez says. “We make sure that this algorithm gets it right for these training examples. Then the hope is that we’ve given it enough training examples that, when we give it a new object, it will also predict the c要么rect label.”

Last July, the team packed up the 2-ton robot and shipped it to Japan, where, a month later, they reassembled it to participate in the 亚马逊机器人挑战赛, a yearly competition spons要么ed by the online megaretailer to encourage innovations in warehouse technology. Rodriguez’s team was one of 16 taking part in a competition to pick and stow objects from a cluttered bin.

In the end, the team’s robot had a 54 percent success rate in picking objects up using suction and a 75 percent success rate using grasping, and was able to recognize novel objects with 100 percent accuracy. The robot also stowed all 20 objects within the allotted time.

For his work, Rodriguez was recently granted an Amazon 研究 Award and will be working with the company to further improve pick-and-place technology — f要么emost, its speed and reactivity.

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The team has already taken some steps toward this goal by adding tactile sens要么s to the robot’s gripper and running the system through a new training regime.

“The gripper now has tactile sensors, and we’ve enabled a system where the robot spends all day continuously picking things from one place to another. It’s capturing information about when it succeeds and fails, and how it feels to pick up, or fails to pick up objects,” Rodriguez says. “Hopefully it will use that inf要么mation to start bringing that reactiveness to grasping.”

This research was sponsored in part by ABB Inc., Mathw要么ks, and Amazon.


主题: 算法, 机器学习, 机械工业, 研究, 机器人, 机器人, 工程学院, 人工智能

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