MOST: Memory of Object States and Task Progress for Long-Horizon Mobile Manipulation
Overview
This research presents MOST, a framework designed to enhance long-horizon mobile manipulation by compressing multi-view observations into temporal tokens that explicitly track object states and task progress. The authors introduce the RoboCasa-Long benchmark for multi-minute, multi-stage tasks. Experiments on RoboCasa365, RoboCasa-Long, and real-world robots show MOST outperforming the previous state-of-the-art model (Xiaomi-Robotics-1), raising average success rates from 65.9% to 72.1% on RoboCasa365, maximum task progress from 20.83% to 42.22% on RoboCasa-Long, and real-world success from 6.66% to 43.33%.
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- arXiv Robotics — research abstractsSignalMOST: Memory of Object States and Task Progress for Long-Horizon Mobile Manipulation
This research presents MOST, a framework designed to enhance long-horizon mobile manipulation by compressing multi-view observations into temporal tokens that explicitly track object states and task progress. The authors introduce the RoboCasa-Long benchmark for multi-minute, multi-stage tasks. Experiments on RoboCasa365, RoboCasa-Long, and real-world robots show MOST outperforming the previous state-of-the-art model (Xiaomi-Robotics-1), raising average success rates from 65.9% to 72.1% on RoboCasa365, maximum task progress from 20.83% to 42.22% on RoboCasa-Long, and real-world success from 6.66% to 43.33%.
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