When completing tasks on the Axis platform, many users focus solely on achieving high scores for individual trajectories. Through publicly available collaboration materials from Axis and Booster, their concrete business model becomes clear: Axis delivers services to clients using a bundled pricing model of 5–20 tasks per package; robotic companies purchase pre-validated, comprehensive task bundles. For example, basic tasks such as picking up a pen or grasping a headphone case involve multiple variations under each task—including object position, lighting conditions, camera angles, and motion trajectories—to train robots to adapt to real-world variability. The division of labor is clearly defined: Booster provides the robotic hardware and handles hardware integration, equipment calibration, and physical validation. @axisrobotics integrates the robots into the simulation platform, builds corresponding simulated environments, and leverages the community to generate large volumes of operational trajectory data. The generated data first undergoes simulation validation, followed by joint physical testing with Booster to complete a closed-loop data cycle between simulation and reality. Public experimental data clearly demonstrates the performance gains from simulated data: Using only 10 real-world demonstration examples, the robot achieved 0 successful contacts in 20 physical tests. However, when combined with 50 simulated demonstrations, the same robot achieved 17 successful contacts in 20 physical tests. Another low-sample experiment showed that after fine-tuning Booster’s model with Axis’s dataset, performance using just 30 demonstration samples per task surpassed the original model’s performance using 60 samples. Note: These experimental results apply only to the specific tasks and evaluation conditions selected in this study and cannot be directly generalized to all robotic applications. However, they demonstrate that high-quality simulated data can significantly reduce the costly effort required for physical data collection. Based on this logic, when participating in Axis tasks, prioritizing broad coverage across scenarios and skills is more valuable than repeatedly refining a single high-scoring trajectory. Once a high-score trajectory is achieved for a simple task, it is advisable to stop repeating the same operation. Instead, switch object types, environmental conditions, or difficulty levels to generate diverse data that better aligns with customers’ actual needs when purchasing “task packages.” The grasping and placement operations performed in the browser take only minutes per attempt, but these trajectories must still be organized, validated, and packaged into task bundles before entering robotic manufacturers’ model training pipelines. New participants should start with Beginner-level tasks and ensure they sign their completed tasks in their Portfolio. Trajectories without a Base chain signature will not be counted toward platform points. https://t.co/EG13vQL9M5