@rohanpaul_ai: 10,000 parcels. 5 hours, 14 minutes. One embodied AI model running the whole challenge. X Square Robot's WALL-B model s…

10,000 parcels. 5 hours, 14 minutes. One embodied AI model running the whole challenge. X Square Robot's WALL-B model sustained 1,911 parcels/hour, or about 1.88 seconds/parcel, while sorting 10,000 packages. The task is deceptively physical. The arm has to identify package orientation, flip each parcel label-side up, then slide it onto the conveyor. Odd objects such as soft toys are routed separately. For context, Figure has reported a 2.88-second parcel cycle time. X Square Robot's WALL-B model decides how each parcel should be handled from the scene, while its six-axis arms execute the picks and corrections. Its previous public run averaged 1,816 parcels an hour with over 98% accuracy, so the new result claims both longer duration and higher throughput. The apparent simplicity of picking a parcel hides a repeated closed-loop computation spanning 3D perception, grasp reasoning, motion planning, force control, and online correction. The difficulty comes from combining fast visual perception, 3D geometry, grasp selection, collision-aware motion planning, feedback control, and failure recovery under a scene that changes after every action. Sustaining the full loop across 10,000 parcels tests whether the robotics stack remains stable when thousands of small physical uncertainties accumulate. Another point of difficulty comes from the endurance, because this is as difficult as the speed. Every additional hour gives perception errors, tiny calibration errors, grasp failures, awkward package geometries, and small control mistakes more opportunities to compound. A system that looks great for 50 parcels can behave very differently after several thousand. The footage also shows the policy doing more than repetitive pick-and-place. Packages are flipped label-side up, moved onto the conveyor, and unusual items such as soft toys are sent to another lane.