ALAM: Algebraically Consistent Latent Transitions for Vision-Language-Action Models
Zuojin Tang, Haoyun Liu, Xinyuan Chang, Changjie Wu, Zhenyang Shi · · 2026
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Summary
Vision-Language-Action (VLA) models map visual observations and language instructions to robot actions through learned l...
Abstract Summary
Key Points
- Proposes ALAM, a novel approach for vla in robotics.
- Addresses key limitations in existing methods through innovative architecture design.
- Demonstrates strong empirical results on standard benchmarks and real-world evaluations.
- Introduces novel training methodology that improves generalization and sample efficiency.
- Provides comprehensive analysis of failure modes and ablation studies.
Abstract
Vision-Language-Action (VLA) models map visual observations and language instructions to robot actions through learned latent representations. This paper proposes ALAM, which enforces group-theoretic constraints on latent transitions to ensure compositional consistency and reliable multi-step reasoning.
Links
- Paper (PDF): 2605.10819
- arXiv: 2605.10819
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