Grasp success prediction with quality metrics
This work addresses the need for reliable grasp prediction in robotic manipulation, though it appears incremental as it builds on existing metrics and methods.
The paper tackled the problem of predicting robotic grasp success by combining multiple metrics and training a classifier, achieving a 76% success rate in experiments with different grippers and objects in simulation and real-world settings.
Current robotic manipulation requires reliable methods to predict whether a certain grasp on an object will be successful or not prior to its execution. Different methods and metrics have been developed for this purpose but there is still work to do to provide a robust solution. In this article we combine different metrics to evaluate real grasp executions. We use different machine learning algorithms to train a classifier able to predict the success of candidate grasps. Our experiments are performed with two different robotic grippers and different objects. Grasp candidates are evaluated in both simulation and real world. We consider 3 different categories to label grasp executions: robust, fragile and futile. Our results shows the proposed prediction model has success rate of 76\%.