R2RGen introduces a simulator-free three-stage pipeline that parses, augments, and post-processes real pointcloud observation-action pairs to improve spatial generalization in robotic manipulation policies.
Validation performance plateaued after approximately 2,500 epochs, and we selected the checkpoint with the lowest validation loss
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R2RGEN: Real-to-Real 3D Data Generation for Spatially Generalized Manipulation
R2RGen introduces a simulator-free three-stage pipeline that parses, augments, and post-processes real pointcloud observation-action pairs to improve spatial generalization in robotic manipulation policies.