<ul data-eligibleForWebStory="true"><li>Warehouse automation using machine learning models can enhance operational efficiency and reduce costs in large-scale robotic fleets.</li><li>Current research focuses on increasing picking success rates by prioritizing high-probability picks but lacks data-driven optimization for performance at scale.</li><li>A new ML-based framework was developed to predict transform adjustments and optimize suction cup selection for multi-suction end effectors in packages.</li><li>The framework was tested in workcells resembling Amazon Robotics' Robot Induction fleet, leading to a 20% decrease in pick failure rates compared to heuristic methods.</li></ul>

Learning to Optimize Package Picking for Large-Scale, Real-World Robot Induction

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