MVP-LAM: Learning Action-Centric Latent Action via Cross-Viewpoint Reconstruction

• URL: http://arxiv.org/abs/2602.03668v1
• Date: Tue, 03 Feb 2026 15:51:25 GMT
• Title: MVP-LAM: Learning Action-Centric Latent Action via Cross-Viewpoint Reconstruction
• Abstract summary: We propose textbfMulti-textbfViewtextbfPoint textbfLatent textbfAction textbfModel (textbfMVP-LAM)<n>MVP-LAM learns discrete latent actions that are highly informative about ground-truth actions from time-synchronized multi-view videos.<n>On Bridge V2, MVP-LAM produces more action-centric latent actions, achieving higher mutual information with ground-truth actions and improved action prediction.
• Score: 11.87290433247564
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Learning \emph{latent actions} from diverse human videos enables scaling robot learning beyond embodiment-specific robot datasets, and these latent actions have recently been used as pseudo-action labels for vision-language-action (VLA) model pretraining. To make VLA pretraining effective, latent actions should contain information about the underlying agent's actions despite the absence of ground-truth labels. We propose \textbf{M}ulti-\textbf{V}iew\textbf{P}oint \textbf{L}atent \textbf{A}ction \textbf{M}odel (\textbf{MVP-LAM}), which learns discrete latent actions that are highly informative about ground-truth actions from time-synchronized multi-view videos. MVP-LAM trains latent actions with a \emph{cross-viewpoint reconstruction} objective, so that a latent action inferred from one view must explain the future in another view, reducing reliance on viewpoint-specific cues. On Bridge V2, MVP-LAM produces more action-centric latent actions, achieving higher mutual information with ground-truth actions and improved action prediction, including under out-of-distribution evaluation. Finally, pretraining VLAs with MVP-LAM latent actions improves downstream manipulation performance on the SIMPLER and LIBERO-Long benchmarks.

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