ARSTAG: An Agentic Real2Sim2Real System for Task-Specific Robot Data Generation

Abstract:Adapting visuomotor policies to new manipulation tasks often requires substantial manual engineering or teleoperated data collection. Simulation can provide task-specific data at scale, but constructing the scene, designing expert behavior, and configuring data generation still require significant per-task effort. We present ARSTAG, an agentic Real2Sim2Real system that turns a single RGB image and a natural-language instruction directly into robot policy-learning data. A hierarchy of language agents constructs a task-scoped simulation scene, generates robot-feasible demonstrations, and expands the training distribution through task-consistent randomization, while a coordinator agent manages cross-stage feedback and recovery. Across seven manipulation tasks spanning grasping, placement, and stacking, the ARSTAG-generated demonstrations enable sim-to-real transfer of three visuomotor policy architectures to a dual-arm robot, with pi0.5 achieving an average real-world success rate of 74.6%. Ablations show that task-consistent randomization substantially improves robustness, and policy performance increases with generated dataset size. Project webpage: this https URL.

12 pages, 15 figures and tables. Project webpage: this https URL

Robotics (cs.RO)

arXiv:2609.24563 [cs.RO]

(or arXiv:2609.24563v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2609.24563 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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