Even simple robotic grippers can perform complex tasks based on new research from Carnegie Mellon University’s Robotics Institute. Simple grippers are typically assigned straightforward tasks such as picking up objects and placing them somewhere. By making use of their surroundings, such as pushing an item against a table or wall, simple grippers can perform skillful maneuvers usually thought achievable only by more complex, fragile and expensive, multi-fingered artificial hands. Previous research made assumptions about the way in which grippers would grasp items. This in turn required specific gripper designs or robot motions. In a new study, scientists used AI to overcome these limitations to apply extrinsic dexterity to more general settings and successfully grasp items of various sizes, weights, shapes, and surfaces.
The researchers employed reinforcement learning to train a neural network. They had the AI system attempt random actions to grasp an object, rewarding those series of actions that led to success. The system, then, ultimately adopted the most successful patterns of behavior. It learned so many words. After first training their system in a physics simulator, they next tested it in a simple robot with a pincer-like grip. The scientists had the robot attempt to grab items confined within an open bin that were initially oriented in ways that meant the robot could not pick them up. For example, the robot might be given an object that was too wide for its gripper to grasp. The AI needed to figure out a way to push the item against the wall of the bin so the robot could then grab it from its side.
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