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Reducing the demonstration burden for robotic manipulation tasks by leveraging atomic skills learned from human demonstrations could revolutionize how robots learn and execute complex actions.
Training on a new multi-object dataset with explicit modeling of grasp offsets and pre-grasp configurations enables an end-to-end network to achieve significantly improved dexterous grasping performance in simulation and on a real robot.
