发表机构
Tsinghua University; OpenMind (WuHu) Robotics Co., Ltd.（清华大学; 芜湖悟灵机器人有限公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究机器人装配等封闭世界任务中数据与精度的关系，提出新缩放定律log(N) ∝ 1/(P - c)，揭示极限精度c是系统涌现属性，经实验验证，为精度提供理论框架和评估指标，指导高精度操作系统开发调试。
AI 中文摘要
虽然模仿学习的缩放定律主要关注开放世界环境中的泛化，但机器人装配等封闭世界任务中数据与精度之间的关系在很大程度上仍未得到探索。本文系统地研究了这种关系并引入了一种新的缩放定律。我们发现，为了达到固定的成功率，随着目标精度P接近极限c，所需的示范数量N呈超指数增长。这种关系由模型log(N) ∝ 1/(P - c)准确捕捉。关键的是，我们揭示极限精度c不是任务的静态物理常数，而是整个智能体系统（包括其传感器和专家策略）的涌现属性。通过对典型操作任务的实验，我们验证了这条定律，并表明改进系统组件（如添加腕部摄像头或使用更有效的专家）可显著降低c，从而扩展系统可实现的精度。我们的工作为机器人技术中的精度提供了一个新的理论框架和一个评估系统能力的定量指标。此外，这些发现为指导高精度操作系统的开发和调试提供了一种实用方法。
英文摘要
While scaling laws for imitation learning have primarily focused on generalization in open-world settings, the relationship between data and precision in closed-world tasks like robotic assembly remains largely unexplored. This paper systematically investigates this relationship and introduces a novel scaling law. We find that to achieve a fixed success rate, the required number of demonstrations $N$ grows super-exponentially as the target precision $P$ approaches a limit $c$. This relationship is accurately captured by the model $\log(N) \propto 1/(P-c)$. Crucially, we reveal that the limit precision $c$ is not a static physical constant of the task but an emergent property of the entire agent system, including its sensors and expert policy. Through experiments on canonical manipulation tasks, we validate this law and demonstrate that improving system components, such as adding a wrist camera or using a more effective expert, measurably lowers $c$, thus expanding the system's achievable precision. Our work provides a new theoretical framework for precision in robotics and a quantitative metric to evaluate system capabilities. Furthermore, these findings provide a practical methodology for guiding the development and debugging of high-precision manipulation systems.
Comments8 pages, 4 figures. Accepted to the 2026 IEEE International Conference on Robotics and Automation (ICRA 2026)
