HiVLA: A Visual-Grounded-Centric Hierarchical Embodied Manipulation System

• URL: http://arxiv.org/abs/2604.14125v1
• Date: Wed, 15 Apr 2026 17:50:07 GMT
• Title: HiVLA: A Visual-Grounded-Centric Hierarchical Embodied Manipulation System
• Abstract summary: HiVLA is a visual-grounded-centric hierarchical framework that explicitly decouples high-level semantic planning from low-level motor control.<n>Experiments in simulation and the real world demonstrate that HiVLA significantly outperforms state-of-the-art end-to-end baselines.
• Score: 48.02804864175127
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While end-to-end Vision-Language-Action (VLA) models offer a promising paradigm for robotic manipulation, fine-tuning them on narrow control data often compromises the profound reasoning capabilities inherited from their base Vision-Language Models (VLMs). To resolve this fundamental trade-off, we propose HiVLA, a visual-grounded-centric hierarchical framework that explicitly decouples high-level semantic planning from low-level motor control. In high-level part, a VLM planner first performs task decomposition and visual grounding to generate structured plans, comprising a subtask instruction and a precise target bounding box. Then, to translate this plan into physical actions, we introduce a flow-matching Diffusion Transformer (DiT) action expert in low-level part equipped with a novel cascaded cross-attention mechanism. This design sequentially fuses global context, high-resolution object-centric crops and skill semantics, enabling the DiT to focus purely on robust execution. Our decoupled architecture preserves the VLM's zero-shot reasoning while allowing independent improvement of both components. Extensive experiments in simulation and the real world demonstrate that HiVLA significantly outperforms state-of-the-art end-to-end baselines, particularly excelling in long-horizon skill composition and the fine-grained manipulation of small objects in cluttered scenes.

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