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Two different objects (cube and sphere) made from two soft materials (foam and rubber) are tested in order to verify that the proposed model can represent their non-linear deformations and that the proposed grasp strategy can implement a robust grasp of them with a multi-fingered robotic hand equipped with tactile sensors.
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In the context of object interaction and manipulation, one characteristic of a robust grasp is its ability to comply with external perturbations applied to the grasped object while still maintaining the grasp.
When this dual learning system stabilizes, the model is endowed with a set of affordance extraction and robust grasp planning mechanisms.
Several methods for robust grasp planning exploit the object geometry or tactile sensor feedback.
This paper considers these merits of caging and proposes an efficient approach to find the optimized finger positions for robust grasping under perception noises by measuring the quality of caging.
Current endoscopes and instruments are inadequate to perform such complex surgeries for a variety of reasons: they are too flexible and are insufficient to provide robust grasping and anatomic retraction.
To obtain robust grasping, we attempted to search for appropriate grasping postures.
However, object pose range estimation introduces specific uncertainties that can also be exploited to choose more robust grasps.
In the experiments, the operator directly controls the (simulated) robot using visual feedback to achieve robust grasping with the robot.
Good grasp of technology.
His grasp of economics is shaky.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com