HiVLA: A Visual-Grounded-Centric Hierarchical Embodied Manipulation System
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arXiv
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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866911669011087360 |
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| author | Yang, Tianshuo Chen, Guanyu Chen, Yutian Liang, Zhixuan Liu, Yitian Chen, Zanxin Xu, Chunpu Liang, Haotian Pang, Jiangmiao Mu, Yao Luo, Ping |
| author_facet | Yang, Tianshuo Chen, Guanyu Chen, Yutian Liang, Zhixuan Liu, Yitian Chen, Zanxin Xu, Chunpu Liang, Haotian Pang, Jiangmiao Mu, Yao Luo, Ping |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2604_14125 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | HiVLA: A Visual-Grounded-Centric Hierarchical Embodied Manipulation System Yang, Tianshuo Chen, Guanyu Chen, Yutian Liang, Zhixuan Liu, Yitian Chen, Zanxin Xu, Chunpu Liang, Haotian Pang, Jiangmiao Mu, Yao Luo, Ping Computer Vision and Pattern Recognition Artificial Intelligence Robotics 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. |
| title | HiVLA: A Visual-Grounded-Centric Hierarchical Embodied Manipulation System |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Robotics |
| url | https://arxiv.org/abs/2604.14125 |