ART turns VLA models into tool-calling robot agents, reporting a 20% higher success rate than mainstream baselines in simulation and real-world tasks
The work proposes Agentic Robot with Tool-use (ART), 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; the authors built a dataset of 30K tool-use trajectories and action demonstrations and designed a training regimen for long-trajectory tool-use reasoning in challenging environments, with experiments showing 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.