AWML为机器人感知提供开源MLOps框架，支持自动驾驶模型部署与主动学习。
AWML: An Open-Source ML-based Robotics Perception Framework to Deploy for ROS-based Autonomous Driving Software
- 构建基于ROS的机器学习基础设施，实现模型部署与持续优化
- 集成自动标注、半自动标注与数据挖掘，提升标注效率
- 适合自动驾驶研发团队快速搭建闭环学习系统
近年来，机器学习技术在机器人领域，特别是自主机器人与自动驾驶车辆的开发中发挥了重要作用。随着行业成熟，如ROS 2等机器人框架已从研究走向生产应用。本文介绍AWML，一个面向机器人MLOps的开源框架，旨在支持自动驾驶中的机器学习工作流。AWML不仅支持训练好的模型在机器人系统中的部署，还提供主动学习流水线，集成自动标注、半自动标注与数据挖掘技术，有效提升数据标注效率与模型迭代速度。
原文摘要 · Abstract (English)
In recent years, machine learning technologies have played an important role in robotics, particularly in the development of autonomous robots and self-driving vehicles. As the industry matures, robotics frameworks like ROS 2 have been developed and provides a broad range of applications from research to production. In this work, we introduce AWML, a framework designed to support MLOps for robotics. AWML provides a machine learning infrastructure for autonomous driving, supporting not only the deployment of trained models to robotic systems, but also an active learning pipeline that incorporates auto-labeling, semi-auto-labeling, and data mining techniques.
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