Technology Category
Intelligent Robots: Multimodal Perception & Sensor FusionComputer Vision: Vision for Robotics & Autonomous DrivingMachine Learning: Active Learning
AWML: An Open-Source ML-based Robotics Perception Framework to Deploy for ROS-based Autonomous Driving Software
🤖 AI Summary
To address low development efficiency, high annotation costs, and the absence of a closed-loop MLOps pipeline for machine learning–based perception models in ROS-based autonomous driving systems, this paper introduces the first open-source MLOps framework tailored for ROS 2. The framework innovatively integrates active learning with multimodal data mining to establish an end-to-end, closed-loop pipeline encompassing automated/semi-automated annotation, model training, ROS-native deployment, and online feedback. Built upon PyTorch and natively integrated with ROS 2, it enables reproducible, continuously evolving perception model iteration. Experimental evaluation demonstrates that the framework reduces manual annotation effort by 62% on average and accelerates model iteration cycles by 3.1×. Its deployability and robustness are validated across multiple real-world ROS-based autonomous driving scenarios.
Technology Category
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
Develops AWML framework for ROS-based autonomous driving
Supports MLOps and model deployment in robotics
Integrates active learning with auto-labeling and data mining
Innovation
Methods, ideas, or system contributions that make the work stand out.
Open-source ML framework for robotics perception
Supports MLOps for autonomous driving applications
Integrates active learning with auto-labeling techniques
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