Evolve Vision-Language-Action Model into an Agent with On-the-fly Tool-use
arXiv:2608.14047v1 Announce Type: new Abstract: This paper integrates end-to-end Visual-Language-Action (VLA) models with agentic tool-use to propose Agentic Robot with Tool-use (ART). ART is a tool-injection framework that tunes any VLA model to leverage off-the-shelf tool modules for low-level vision, high-level affordance, and embodiment enhancement. Compared to vanilla VLA models with a whole continuous action solution space, ART reduces the complexity of the action solution space through tool-use, which not only improves generalizability across different tasks but also reduces data dependency. To demonstrate the advantages (high generalizability and low data dependency) of this framework, we first built a dataset of 30K tool-use trajectories and action demonstrations, which is much smaller than those used by baseline methods. We then designed a training regimen for long-trajectory tool-use reasoning in challenging environments. Experiments show that ART achieves a 20% higher success rate than mainstream baselines on simulation and real-world tasks, such as pick-and-place in the dark at novel viewpoints. Empirical results highlight the benefits of an agent-based approach: modular tool utilization enables more efficient training, lightweight deployment, and scalable integration of new tools. This design fosters robustness, adaptability, and extensibility, paving the way for the practical deployment of VLA systems in complex real-world scenarios.
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[Submitted on 14 Aug 2026]
Title:Evolve Vision-Language-Action Model into an Agent with On-the-fly Tool-use
View a PDF of the paper titled Evolve Vision-Language-Action Model into an Agent with On-the-fly Tool-use, by Yi Ding and 7 other authors
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Abstract:This paper integrates end-to-end Visual-Language-Action (VLA) models with agentic tool-use to propose Agentic Robot with Tool-use (ART). ART is a tool-injection framework that tunes any VLA model to leverage off-the-shelf tool modules for low-level vision, high-level affordance, and embodiment enhancement. Compared to vanilla VLA models with a whole continuous action solution space, ART reduces the complexity of the action solution space through tool-use, which not only improves generalizability across different tasks but also reduces data dependency. To demonstrate the advantages (high generalizability and low data dependency) of this framework, we first built a dataset of 30K tool-use trajectories and action demonstrations, which is much smaller than those used by baseline methods. We then designed a training regimen for long-trajectory tool-use reasoning in challenging environments. Experiments show that ART achieves a 20% higher success rate than mainstream baselines on simulation and real-world tasks, such as pick-and-place in the dark at novel viewpoints. Empirical results highlight the benefits of an agent-based approach: modular tool utilization enables more efficient training, lightweight deployment, and scalable integration of new tools. This design fosters robustness, adaptability, and extensibility, paving the way for the practical deployment of VLA systems in complex real-world scenarios.
Comments: 12 pages, 4 figures, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern (CVPR) Findings
Subjects:
Robotics (cs.RO); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2608.14047 [cs.RO]
(or arXiv:2608.14047v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2608.14047
arXiv-issued DOI via DataCite (pending registration)
Journal reference: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern (CVPR) Findings Recognition (CVPR) Findings, 2026, pp. 1346-1357
Submission history
From: Ding Yi [view email] [v1] Fri, 14 Aug 2026 07:53:18 UTC (8,602 KB)
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