World’s most advanced robotic hand is approaching human-level dexterity

Remember when the idea of a robotic hand was a clunky mitt that could do little more than crush things in its iron grip? Well, such clichés should be banished for good based on some impressive work coming out of the WMG department at the U.K.’s University of Warwick.

If the research lives up to its potential, robot hands could pretty soon be every bit as nimble as their flesh-and-blood counterparts. And it’s all thanks to some impressive simulation-based training, new A.I. algorithms, and the Shadow Robot Dexterous Hand created by the U.K.-based Shadow Robot Company (which Digital Trends has covered in detail before.)

Researchers at WMG Warwick have developed algorithms that can imbue the Dexterous Hand with impressive manipulation capabilities, enabling two robot hands to throw objects to one another or spin a pen around between their fingers.

“The Shadow Robot Company [is] manufacturing a robotic hand that is very similar to the human hand,” Giovanni Montana, professor of Data Science, told Digital Trends. “However, so far this has mostly been used for teleoperation applications, where a human operator controls the hand remotely. Our research aims at giving the hand the ability to learn how to manipulate objects on its own, without human intervention. In terms of demonstrating new abilities, we’ve focused on hand manipulation tasks that are deemed very difficult to learn.”

In a paper titled “Solving Challenging Dexterous Manipulation Tasks With Trajectory Optimisation and Reinforcement Learning,” the Warwick researchers created 3D simulations of the hands using a physics engine called MuJoCo (Multi-Joint Dynamics with Contact) that was developed at the University of Washington.

The work — which is currently still in progress — is impressive because it showcases robot hand tasks requiring two hands, such as catching. This adds extra difficulty to the learning process. The researchers think the algorithms represent one of the most impressive examples to date of autonomously learning to complete challenging, dexterous manipulation tasks.

reference

content editor at zino.

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