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.
@article{arxiv.2506.00645,
title = {AWML: An Open-Source ML-based Robotics Perception Framework to Deploy for ROS-based Autonomous Driving Software},
author = {Satoshi Tanaka and Samrat Thapa and Kok Seang Tan and Amadeusz Szymko and Lobos Kenzo and Koji Minoda and Shintaro Tomie and Kotaro Uetake and Guolong Zhang and Isamu Yamashita and Takamasa Horibe},
journal= {arXiv preprint arXiv:2506.00645},
year = {2025}
}