Reins: Frontier Multimodal Models Driving Real Robot Arms on Long-Horizon Tasks, Zero-Shot

Engineering notes

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Chinese explanation / 中文解读

中文解读待补充：本站会优先为 VLA、具身智能、人形机器人控制、机器人操作等高价值论文补充中文说明。

Original abstract

Frontier models such as GPT-6.1 Sol, GPT-6 Astra and Claude Opus 5.5 can now plan and call tools, yet they still struggle to drive real robots through long-horizon, contact-rich manipulation, blind while the arm moves and while they think. We present Reins, a harness in which an off-the-shelf model operates a dual-arm robot with 3D perception tools and atomic actions that report contact and grip. The model chains actions in one call, each with a condition that must hold, stopping at the first that fails; a second instance, the narrator, watches the cameras continuously and reports what changed with every result. From images, Reins also builds a simulation-ready twin of the scene for continual policy learning. With natural-language prompts and no task-specific code, GPT-6.1 Sol succeeds in 88% of real episodes on five tasks; without the harness, it fails every shell game, taking 8 times as long. Three frontier models perform alike but differ in tracking a scene that changes while the actor thinks. Code will be released upon publication.

Links and sources

• Official / arXiv page
• PDF from original source

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