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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.
It is shown that a flexible and expressive transformer, PointZero, outperforms prior methods on the same data and demonstrates the utility of the pre-training objective by post-training PointZero for two downstream applications: action-conditioned 3D dynamics prediction and imitation learning.
This work introduces Wiggle and Go!, a two-stage framework for zero-shot rope manipulation: a brief, safe wiggle action is observed to predict descriptive rope parameters, which then conditions a trajectory optimizer for zero-shot goal-conditioned execution.
This work introduces SPAR3S, a sparse voxel-aligned 3D latent generative model for conditional scene completion without requiring ground-truth 3D data for supervision, and trains a masked autoregressive transformer that jointly models voxel occupancy and latent token values, enabling efficient and spatially consistent...
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