Graph-Fused Vision-Language-Action for Policy Reasoning in Multi-Arm Robotic Manipulation
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866916943148089344 |
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| author | Li, Shunlei Gao, Longsen Cao, Jiuwen Hu, Yingbai |
| author_facet | Li, Shunlei Gao, Longsen Cao, Jiuwen Hu, Yingbai |
| contents | Acquiring dexterous robotic skills from human video demonstrations remains a significant challenge, largely due to conventional reliance on low-level trajectory replication, which often fails to generalize across varying objects, spatial layouts, and manipulator configurations. To address this limitation, we introduce Graph-Fused Vision-Language-Action (GF-VLA), a unified framework that enables dual-arm robotic systems to perform task-level reasoning and execution directly from RGB-D human demonstrations. GF-VLA employs an information-theoretic approach to extract task-relevant cues, selectively highlighting critical hand-object and object-object interactions. These cues are structured into temporally ordered scene graphs, which are subsequently integrated with a language-conditioned transformer to produce hierarchical behavior trees and interpretable Cartesian motion primitives. To enhance efficiency in bimanual execution, we propose a cross-arm allocation strategy that autonomously determines gripper assignment without requiring explicit geometric modeling. We validate GF-VLA on four dual-arm block assembly benchmarks involving symbolic structure construction and spatial generalization. Empirical results demonstrate that the proposed representation achieves over 95% graph accuracy and 93% subtask segmentation, enabling the language-action planner to generate robust, interpretable task policies. When deployed on a dual-arm robot, these policies attain 94% grasp reliability, 89% placement accuracy, and 90% overall task success across stacking, letter-formation, and geometric reconfiguration tasks, evidencing strong generalization and robustness under diverse spatial and semantic variations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_07957 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Graph-Fused Vision-Language-Action for Policy Reasoning in Multi-Arm Robotic Manipulation Li, Shunlei Gao, Longsen Cao, Jiuwen Hu, Yingbai Robotics Acquiring dexterous robotic skills from human video demonstrations remains a significant challenge, largely due to conventional reliance on low-level trajectory replication, which often fails to generalize across varying objects, spatial layouts, and manipulator configurations. To address this limitation, we introduce Graph-Fused Vision-Language-Action (GF-VLA), a unified framework that enables dual-arm robotic systems to perform task-level reasoning and execution directly from RGB-D human demonstrations. GF-VLA employs an information-theoretic approach to extract task-relevant cues, selectively highlighting critical hand-object and object-object interactions. These cues are structured into temporally ordered scene graphs, which are subsequently integrated with a language-conditioned transformer to produce hierarchical behavior trees and interpretable Cartesian motion primitives. To enhance efficiency in bimanual execution, we propose a cross-arm allocation strategy that autonomously determines gripper assignment without requiring explicit geometric modeling. We validate GF-VLA on four dual-arm block assembly benchmarks involving symbolic structure construction and spatial generalization. Empirical results demonstrate that the proposed representation achieves over 95% graph accuracy and 93% subtask segmentation, enabling the language-action planner to generate robust, interpretable task policies. When deployed on a dual-arm robot, these policies attain 94% grasp reliability, 89% placement accuracy, and 90% overall task success across stacking, letter-formation, and geometric reconfiguration tasks, evidencing strong generalization and robustness under diverse spatial and semantic variations. |
| title | Graph-Fused Vision-Language-Action for Policy Reasoning in Multi-Arm Robotic Manipulation |
| topic | Robotics |
| url | https://arxiv.org/abs/2509.07957 |