Modeling Robotics Dataset Construction as an Artifact-Based Build Process
Researchers have developed Bagzel, an open-source Bazel extension designed to streamline the creation of machine learning datasets from robotic sensor data. By modeling dataset construction as an artifact-based build process, Bagzel aims to reduce the engineering overhead and slow iteration cycles typically associated with converting ROS bag recordings. Evaluations show that Bagzel significantly reduces runtime, particularly in iterative and incremental build scenarios, offering substantial improvements over traditional sequential scripting methods. AI
IMPACT Enhances reproducibility and efficiency in ML dataset generation for robotics, potentially accelerating research and development.