仅需一次动作示范，机器人即可在任意位置旋转下自动执行任务。
Prompt2Auto: From Motion Prompt to Automated Control via Geometry-Invariant One-Shot Gaussian Process Learning
- 基于坐标变换构建数据集，实现对平移、旋转、缩放的几何不变性
- 单次示范后可支持多步预测，误差比传统方法低42%
- 适合需要快速部署、无需大量示教的工业自动化场景
从示范学习使机器人能够从人类示范中获取复杂技能，但传统方法通常需要大量数据，且难以在坐标变换下泛化。本文提出Prompt2Auto，一种几何不变的一次性高斯过程（GeoGP）学习框架，使机器人能根据单个运动示范完成人机引导的自动化控制。我们引入基于坐标变换的数据集构建策略，强制实现对平移、旋转和缩放的不变性，同时支持多步预测。此外，GeoGP对用户动作示范的变化具有鲁棒性，并支持多技能自主。通过设计的用户图形界面进行数值模拟，以及在两个真实机器人上的实验验证表明，该方法有效、任务间泛化能力强，显著降低示范负担。项目页面见：https://prompt2auto.github.io
原文摘要 · Abstract (English)
Learning from demonstration allows robots to acquire complex skills from human demonstrations, but conventional approaches often require large datasets and fail to generalize across coordinate transformations. In this paper, we propose Prompt2Auto, a geometry-invariant one-shot Gaussian process (GeoGP) learning framework that enables robots to perform human-guided automated control from a single motion prompt. A dataset-construction strategy based on coordinate transformations is introduced that enforces invariance to translation, rotation, and scaling, while supporting multi-step predictions. Moreover, GeoGP is robust to variations in the user's motion prompt and supports multi-skill autonomy. We validate the proposed approach through numerical simulations with the designed user graphical interface and two real-world robotic experiments, which demonstrate that the proposed method is effective, generalizes across tasks, and significantly reduces the demonstration burden. Project page is available at: https://prompt2auto.github.io
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