Active Tactile Exploration for Grasping

This paper addresses the problem of robotic grasp optimization. Due to uncertainty both on the robot kinematics, motor control and object perception, it is very hard to analytically compute good grasps and execute them successfully. Our approach is based on searching for the best grasp configurations by iteratively optimizing a suitable grasp criterion. This approach may be compared to human learning stages where infants learn by trial and error what are the best grasping strategies. Initial grasps are often unsuccessful, but after a few trials the system learns to adapt to the uncertainties in the environment.

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