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arXiv · 2604.03340

Learning Additively Compositional Latent Actions for Embodied AI

Hangxing Wei·Xiaoyu Chen·Chuheng Zhang·Tim Pearce·Jianyu Chen·Alex Lamb·Li Zhao·Jiang Bian
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Abstract

Latent action learning infers pseudo-action labels from visual transitions, providing an approach to leverage internet-scale video for embodied AI. However, most methods learn latent actions without structural priors that encode the additive, compositional structure of physical motion. As a result, latents often entangle irrelevant scene details or information about future observations with true state changes and miscalibrate motion magnitude. We introduce Additively Compositional Latent Action Model (AC-LAM), which enforces scene-wise additive composition structure over short horizons on the latent action space. These AC constraints encourage simple algebraic structure in the latent action space~(identity, inverse, cycle consistency) and suppress information that does not compose additively. Empirically, AC-LAM learns more structured, motion-specific, and displacement-calibrated latent actions and provides stronger supervision for downstream policy learning, outperforming state-of-the-art LAMs across simulated and real-world tabletop tasks.

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Hangxing Wei, Xiaoyu Chen, Chuheng Zhang, Tim Pearce, Jianyu Chen, Alex Lamb, Li Zhao, Jiang Bian. 2026-04-03. Learning Additively Compositional Latent Actions for Embodied AI. https://arxiv.org/abs/2604.03340

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