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Multi-Fingered Active Grasp Learning
We show that our active grasp learning approach uses fewer training samples to produce grasp success rates comparable with the passive supervised learning method trained with grasping data generated by an analytical planner.
Robotics
Multi-Fingered Grasp Planning via Inference in Deep Neural Networks
We can then formulate grasp planning as inferring the grasp configuration which maximizes the probability of grasp success.
Robotics
Learning Continuous 3D Reconstructions for Geometrically Aware Grasping
We leverage the structure of the reconstruction network to learn a grasp success classifier which serves as the objective function for a continuous grasp optimization.
