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NeRF in the Palm of Your Hand: Corrective Augmentation for Robotics via Novel-View Synthesis

Description

Expert demonstrations are a rich source of supervision for training visual robotic manipulation policies, but imitation learning methods often require either a large number of demonstrations or expensive online expert supervision to learn reactive closed-loop behaviors. In this work, we introduce SPARTN (Synthetic Perturbations for Augmenting Robot Trajectories via NeRF): a fully-offline data augmentation scheme for improving robot policies that use eye-in-hand cameras. Our approach leverages ne

Source

http://arxiv.org/abs/2301.08556v1