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ModAR is introduced, the first WAM to autoregressively denoise multiple future modalities before predicting actions, and it is found that ModAR's sequential generation outperforms existing WAM formulations, with the highest average success rate at all evaluated data scales.
Joint image-depth generation can be achieved with a single model trained on sparse data, outperforming existing methods by a significant margin.
Robots can now learn manipulation skills from ordinary human videos, thanks to a 3D point tracking method that bridges the embodiment gap and requires only 20 robot demonstrations.
