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Fine-tuned robotic policies can be biased towards certain instruction factors, but a new bias-aware data strategy can significantly enhance their performance with fewer demonstrations.
Pix2Act transforms complex 3D manipulation into a simpler 2D prediction task, leading to significant performance gains and robustness against camera variations.
By explicitly reasoning in 3D, VolumeDP leaps ahead of 2D-based imitation learning methods, achieving a remarkable 14.8% improvement on the LIBERO benchmark and robust real-world generalization.
