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

TL;DR

AWML is an open-source framework that integrates ML infrastructure with ROS to enhance autonomous driving robots through deployment, active learning, and data management.

Contribution

It introduces AWML, a novel open-source framework that supports MLOps for robotics, enabling deployment and active learning in autonomous driving systems.

Findings

Supports deployment of trained models to robots

Includes active learning with auto-labeling

Facilitates data mining for autonomous driving

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.