ALAM: Algebraically Consistent Latent Transitions for Vision-Language-Action Models
Researchers have introduced ALAM (Algebraic Latent Action Model), a novel approach designed to enhance Vision-Language-Action (VLA) models by addressing the scarcity of action-labeled robot data. Unlike traditional latent action models that rely on reconstruction but lack structural coherence for policy generation, ALAM leverages abundant action-free videos to learn algebraically consistent latent transitions. By regularizing these transitions with composition and reversal consistency, the model creates a locally additive transition space grounded in temporal relations. This structured latent space is then used as an auxiliary generative target in downstream VLA learning, coupled with robot actions via a joint flow-matching objective. Experimental results demonstrate significant improvements, reducing additivity and reversibility errors by 25-85 times compared to unstructured baselines. Furthermore, ALAM boosts average success rates in robotic tasks, raising performance from 47.9% to 85.0% on MetaWorld MT50 and from 94.1% to 98.1% on LIBERO. The study highlights the synergy between algebraically structured latent transitions and flow-based policy generation, offering a robust solution for improving real-world manipulation tasks without requiring explicit latent-to-action decoding.
Wire timeline
ALAM: Algebraically Consistent Latent Transitions for Vision-Language-Action Models
Researchers have introduced ALAM (Algebraic Latent Action Model), a novel approach designed to enhance Vision-Language-Action (VLA) models by addressing the scarcity of action-labeled robot data. Unlike traditional latent action models that rely on reconstruction but lack structural coherence for policy generation, ALAM leverages abundant action-free videos to learn algebraically consistent latent transitions. By regularizing these transitions with composition and reversal consistency, the model creates a locally additive transition space grounded in temporal relations. This structured latent space is then used as an auxiliary generative target in downstream VLA learning, coupled with robot actions via a joint flow-matching objective. Experimental results demonstrate significant improvements, reducing additivity and reversibility errors by 25-85 times compared to unstructured baselines. Furthermore, ALAM boosts average success rates in robotic tasks, raising performance from 47.9% to 85.0% on MetaWorld MT50 and from 94.1% to 98.1% on LIBERO. The study highlights the synergy between algebraically structured latent transitions and flow-based policy generation, offering a robust solution for improving real-world manipulation tasks without requiring explicit latent-to-action decoding.
cs.AI updates on arXiv.org