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		</div><p>Robotics researchers have created an “ambidextrous” robot they say can learn which type of arm gripper to use when picking up objects, speeding up processing in e-commerce fulfilment centres.</p>
<p>A team from the University of California Berkeley created a system that helps a multi-armed robot analyse items it needs to pick up and quickly determine which type of grip is the most likely to succeed.</p>
<p>The system uses a “reward function” assigned to each type of gripper which the robot then uses to quantify the probability of each type of grip succeeding on a particular item.</p>
<p>The developers said this enabled the system to rapidly decide which grip would most effectively pick up the object.</p>
<p>The researchers said in testing their two-armed robot had achieved a 95% reliability rate and suggested it could one day help speed up processing in e-commerce fulfilment centres.</p>
<p>Jeff Mahler, the research’s lead author and a postdoctoral researcher at the university said: “Any single gripper cannot handle all objects.</p>
<p>“For example, a suction cup cannot create a seal on porous objects such as clothing and parallel-jaw grippers may not be able to reach both sides of some tools and toys.”i</p>
<p>The researchers argued that the system could be useful in warehouses in years to come, as companies increasingly turn to robotics to help fulfil growing order demand.</p>
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