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TECH FRONT THE LATEST RESEARCH AND DEVELOPMENT NEWS IN MANUFACTURING AND TECHNOLOGY t Robot Hand Learns to Get a Grip R


obots today are capable of many tasks, but intricate movements that require dexterous in-hand manipula- tion—rolling, pivoting, bending, sensing friction and other things humans do effortlessly with our hands—have proved diffi cult.


A team of computer scientists and engineers from the University of Washington has built a fi ve-fi ngered robot hand that can both perform dexterous manipulation and learn from its experience without needing humans to direct it.


apply the model to the hardware and real-world tasks like rotating an elongated object. With each attempt, the robot hand gets progressively


more adept at spinning the tube, thanks to machine learn- ing algorithms that help it model both the basic physics involved and also plan which actions it should take to achieve the desired result. The more challenging aspect was designing a hand with enough speed, strength, responsiveness and fl exibility to mimic the basic behaviors of a human hand. The end result was a Shadow Hand skeleton actuated with a custom pneumatic system, which can actu- ally move faster than a human hand. With a cost of $300,000, it’s too expensive for routine commercial or industrial use, but allows the researchers to test innova- tive control strategies. Most recently, the


This fi ve-fi ngered robot hand can learn how to perform dexterous manipulation on its own, rather than having humans program its actions.


“Hand manipulation is one of the hardest problems that


roboticists have to solve,” said lead author Vikash Kumar. “A lot of robots today have pretty capable arms but the hand is as simple as a suction cup or maybe a claw or a gripper.” The UW team spent years developing the hand, as well as a simulation model that enables a computer to analyze movements in real time. In their latest demonstration, they


research team has trans- ferred the software simula- tion models to work on the actual fi ve-fi ngered hand hardware. As the robot hand performs different tasks, the system collects data from various sensors and motion-capture cameras and employs machine learn-


ing algorithms to continually refi ne and develop more realistic models. The next trials will focus on global learning and chal- lenge the hand to manipulate an unfamiliar object. The fi ndings were published in the paper, “Optimal Con-


trol with Learned Local Models: Application to Dexterous Manipulation” and presented at the 2016 IEEE International Conference on Robotics and Automation.


July 2016 | AdvancedManufacturing.org 35


Photo courtesy University of Washington


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