AWML: An Open-Source ML-based Robotics Perception Framework to Deploy for ROS-based Autonomous Driving Software
AWML: An Open-Source ML-based Robotics Perception Framework to Deploy for ROS-based Autonomous Driving Software
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.
Satoshi Tanaka、Samrat Thapa、Kok Seang Tan、Amadeusz Szymko、Lobos Kenzo、Koji Minoda、Shintaro Tomie、Kotaro Uetake、Guolong Zhang、Isamu Yamashita、Takamasa Horibe
自动化技术、自动化技术设备计算技术、计算机技术
Satoshi Tanaka,Samrat Thapa,Kok Seang Tan,Amadeusz Szymko,Lobos Kenzo,Koji Minoda,Shintaro Tomie,Kotaro Uetake,Guolong Zhang,Isamu Yamashita,Takamasa Horibe.AWML: An Open-Source ML-based Robotics Perception Framework to Deploy for ROS-based Autonomous Driving Software[EB/OL].(2025-05-31)[2025-07-18].https://arxiv.org/abs/2506.00645.点此复制
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