Problems with human rights in the workplace? Develop a robot instead!

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 before 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 algorithm 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-recognise” workflow, 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 Alberto Rodriguez, the Walter Henry Gale Career Development Professor in Mechanical Engineering at MIT.

Rodriguez and his colleagues at MIT and Princeton will present a paper detailing their system at the IEEE International Conference on Robotics and Automation. 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 sort 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 recognise 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 behaviours: suctioning onto an object, either vertically, or from the side; gripping the object vertically like the claw in an arcade game; or, for objects that lie flush against a wall, gripping vertically, then using a flexible spatula to slide between the object and the wall.

Rodriguez and his team showed the robot images of bins cluttered with objects, captured from the robot’s vantage point. They then showed the robot which objects were graspable, with which of the four main grasping behaviours, and which were not, marking each example as a success or failure.

They did this for hundreds of examples, and over time, the researchers built up a library of picking successes and failures. They then incorporated this library into a 'deep neural network' — a class of learning algorithms that enables the robot to match the current problem it faces with a successful outcome from the past, based on its library of successes and failures.

“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 behaviours was likely to be successful,” Rodriguez says. “Once it was in the gripper, the object was much easier to recognise, without all the clutter.”

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Image credit: MIT.