AWML: An Open-Source ML-based Robotics Perception Framework to Deploy for ROS-based Autonomous Driving Software

Satoshi Tanaka; Samrat Thapa; Kok Seang Tan; Amadeusz Szymko; Lobos Kenzo; Koji Minoda; Shintaro Tomie; Kotaro Uetake; Guolong Zhang; Isamu Yamashita; Takamasa Horibe

arXiv · 2506.00645

AWML: An Open-Source ML-based Robotics Perception Framework to Deploy for ROS-based Autonomous Driving Software

Satoshi Tanaka·Samrat Thapa·Kok Seang Tan·Amadeusz Szymko·Lobos Kenzo·Koji Minoda·Shintaro Tomie·Kotaro Uetake·Guolong Zhang·Isamu Yamashita·Takamasa Horibe

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Abstract

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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Satoshi Tanaka, Samrat Thapa, Kok Seang Tan, Amadeusz Szymko, Lobos Kenzo, Koji Minoda, Shintaro Tomie, Kotaro Uetake, Guolong Zhang, Isamu Yamashita, Takamasa Horibe. 2025-05-31. AWML: An Open-Source ML-based Robotics Perception Framework to Deploy for ROS-based Autonomous Driving Software. https://arxiv.org/abs/2506.00645

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