BREE: A Low-Cost, Fully Fabricable and Reproducible Platform for Real-World Bimanual Robotic Manipulation

Scaling data has been a key driver of recent advances in embodied intelligence, yet real-world robotic data remain fragmented across heterogeneous embodiments and physical platform realizations. Even nominally identical hardware can produce different observation and action distributions due to variations in calibration, sensing, workspace geometry, and task environments. This makes independently collected data difficult to aggregate and reuse. Here we introduce BREE, a low-cost ($\sim \$600$) and fully reproducible bimanual robotic platform built on dual SO-101 arms. BREE standardizes the embodiment, sensing configuration, workspace, and calibration process, supported by BREE-Assets and BREE-Data. BREE-Assets provides standardized and fully fabricable task objects, fixtures, environmental components, including a dedicated physical calibrator for cross-instance alignment. BREE-Data provides eight standardized bimanual manipulation tasks with demonstrations and evaluation protocols based on BREE-Assets. Extensive cross-instance evaluations demonstrate that policies trained on data from one BREE instance can be directly transferred to another, while aggregating data collected from multiple instances can improve policy performance. That is, BREE exhibits cross-instance transfer and multi-instance scaling properties, providing a practical path toward scalable real-world robot learning across distributed platforms. We will open-source the complete BREE stack.