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This can be effective when the robot's moving parts are consistent and well-defined, like a gripper with three fingers.
With a robotic gripper, a reinforcement learning algorithm might move one finger slightly, learning by trial and error whether that motion earns it a reward.
The findings have great potential for industrial applications, such as providing a next-generation robotic gripper for grasping a variety of irregular objects.
The liquid itself responds to different pressures, changing its compliance to adjust the force of the gripper to be able to pick up a heavy bottle, a delicate egg and a small blueberry, with no additional programming.
The researchers demonstrated the programmability of the liquid by loading the metafluid into a hydraulic robotic gripper and having the gripper pick up a glass bottle, an egg and a blueberry.
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