Would you challenge MIT's latest robot to a friendly game of Jenga? MIT
03 February, 2019, 12:27
So the researchers placed a two-pronged industrial robot arm with a force sensor in its wrist by the Jenga tower and allowed it to explore rather than using traditional machine-learning techniques that could require data from tens of thousands of block-extraction attempts in order to capture every possible scenario. Future robots using this tech could be used to assemble smartphones for instance.
For the uninitiated, Jenga comprises 54 rectangular blocks stacked in 18 layers of three blocks each, with the blocks in each layer oriented perpendicular to the blocks below.
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The bot can push gently against a block as the visual and tactile feedback is assessed from the camera and the cuff. It also considers the outcomes of those moves - specifically, whether a block, in a certain configuration and pushed with a certain amount of force, was successfully extracted or not.
A robot capable of analysing a Jenga tower and understanding the safest block to remove has been unveiled by researchers in the United States.
In what marks significant progress for robotic manipulation of real-world objects, a Jenga-playing machine can learn the complex physics involved in withdrawing wooden blocks from a tower through physical trial and error.
This research was supported, in part, by the National Science Foundation through the National Robotics Initiative.
Robots are an increasingly common feature of everyday life, whether they are cleaning your house or stacking shelves at the grocery store.
In a video, the robot proves that it has actually already mastered the game quite well.
The performance of the robotic arm was also compared with the performance of human players. The experience gained from the project should benefit the industry by making robots more sensitive and precise than they were before.
While a Jenga-playing robot sounds fun, there are plenty of other practical applications for this kind of technology.
"There are many tasks that we do with our hands where the feeling of doing it "the right way" comes in the language of forces and tactile cues", Rodriguez says. Obviously not. Teaching one robot to learn to do a multitude of tasks, and adapt to an ever-changing number of variables, is a much better approach.
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