海角大神

Robots could one day have the ability to assume most, if not all, of the predictable tasks currently done by humans, but only if robots can develop hands similar to those of humans, capable of acute recognition and detection. With this goal in mind, researchers at the Massachusetts Institute of Technology (MIT) recently revealed a robot hand, or gripper, capable of determining what objects it grasps.

Daniela Rus, director of the Distributed Robotics Lab at MIT, along with graduate students Bianca Homberg, Robert Katzschmann, and Mehmet Dogar, developed a 鈥渟oft鈥� robotic hand made from silicone that can be attached to existing robots, such as the Baxter manufacturing robot the team used for testing.

As opposed to 鈥渉ard鈥� hands, or grippers made from metal, the 鈥渟oft hand鈥檚 compliance allows it to pick up objects that a rigid hand is not easily capable of without extensive manipulation planning,鈥� wrote the researchers in their associated . 鈥淭hrough experiments we show that our hand is more successful compared to a rigid hand, especially when manipulating delicate objects that are easily squashed and when grasping an object that requires contacting the static environment.鈥�

This, in itself, isn鈥檛 new. Soft materials, like silicone, have been previously used for grippers due to their superiority over hard materials, particularly in the food-service industry where robotic hands are used to package fruits and vegetables that can easily be damaged or crushed.

, for example, builds robotic grippers for use in 鈥渨arehousing, manufacturing, and food-processing environments where the uncertainty and variety in the weight, size, and shape of products being handled has prevented automated solutions from working in the past.鈥�

But these grippers are programmed for particular objects and are not capable of distinguishing between objects.

鈥淩obots are often limited in what they can do because of how hard it is to interact with objects of different sizes and materials,鈥� Ms. Rus explained in a . 鈥淕rasping is an important step in being able to do useful tasks; with this work we set out to develop both the soft hands and the supporting control and planning systems that make dynamic grasping possible."

MIT鈥檚 gripper has three 鈥渇ingers鈥�, each of which have an embedded sensor and 鈥渃hannels in it that fill with air and allow the hand to wrap itself around an object,鈥� from a thin sheet of paper, to a tennis ball, to a roll of duct tape, reported CNBC. 鈥淥n-board sensors enable the robot to 鈥�

鈥淲hen the gripper hones in an object, the fingers send back location data based on their curvature. Using this data, the robot can and compare it to the existing clusters of data points that represent past objects,鈥� explains MIT. 鈥淲ith just three data points from a single grasp, the robot鈥檚 algorithms can distinguish between objects as similar in size as a cup and a lemonade bottle.鈥�

For now though, the robot isn鈥檛 capable of learning new objects by itself. 鈥淓ngineers have to train the hand to recognize each object it's picking up. They make the robot pick up a new object 10 times and then encode that training information in the robot's software,鈥� CNBC reported.

鈥淚f we want they need to be more adaptive and able to interact with objects whose shape and placement are not precisely known,鈥� Rus says. 鈥淥ur dream is to develop a robot that, like a human, can approach an unknown object, big or small, determine its approximate shape and size, and figure out how to interface with it in one seamless motion.鈥�

The team is presenting their research this week in Hamburg, Germany at the .

Also presenting at the conference are researchers from Carnegie Mellon, who developed a similar robotic hand. However, this team鈥檚 hand has 14 embedded strain sensors in each of the three fingers which allow the robot 鈥渢o determine where its fingertips are in contact and to detect forces of less than a tenth of a newton,鈥� according to a .

鈥淗ands are, in some ways, the 鈥� and giving them fingers that can 鈥榯hink鈥� on their own is a big step towards making them more like our own,鈥� wrote Gizmodo.