Libra-VLA: Achieving Learning Equilibrium via Asynchronous Coarse-to-Fine Dual-System

• URL: http://arxiv.org/abs/2604.24921v1
• Date: Mon, 27 Apr 2026 19:02:46 GMT
• Title: Libra-VLA: Achieving Learning Equilibrium via Asynchronous Coarse-to-Fine Dual-System
• Abstract summary: Vision-Language-Action (VLA) models are a promising paradigm for generalist robotic manipulation.<n>We introduce Libra-VLA, a novel Coarse-to-Fine Dual-System VLA architecture.<n>We show that our approach offers a scalable, robust, and responsive solution for open-world manipulation.
• Score: 15.427164345561417
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Vision-Language-Action (VLA) models are a promising paradigm for generalist robotic manipulation by grounding high-level semantic instructions into executable physical actions. However, prevailing approaches typically adopt a monolithic generation paradigm, directly mapping visual-linguistic features to high-frequency motor commands in a flat, non-hierarchical fashion. This strategy overlooks the inherent hierarchy of robotic manipulation, where complex actions can be naturally modeled in a Hybrid Action Space, decomposing into discrete macro-directional reaching and continuous micro-pose alignment, severely widening the semantic-actuation gap and imposing a heavy representational burden on grounding high-level semantics to continuous actions. To address this, we introduce Libra-VLA, a novel Coarse-to-Fine Dual-System VLA architecture. We explicitly decouple the learning complexity into a coarse-to-fine hierarchy to strike a training equilibrium, while simultaneously leveraging this structural modularity to implement an asynchronous execution strategy. The Semantic Planner predicts discrete action tokens capturing macro-directional intent, while the Action Refiner conditions on coarse intent to generate high-frequency continuous actions for precise alignment. Crucially, our empirical analysis reveals that performance follows an inverted-U curve relative to action decomposition granularity, peaking exactly when the learning difficulty is balanced between the two sub-systems. With the asynchronous design, our approach offers a scalable, robust, and responsive solution for open-world manipulation.

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