A new supervised learning approach to grasp planning in robots

Researchers at the University of Utah have recently developed a probabilistic grasp planner that can explicitly model grasp types to plan high-quality precision and power grasps in real time. Their supervised learning approach, outlined in a paper pre-published on arXiv, can effectively plan both power and precision grasps for a given object.

from News on Artificial Intelligence and Machine Learning http://bit.ly/2HyWU5i

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