Core Insights - The article discusses the launch of RoboCOIN, a high-quality bimanual robotic dataset aimed at overcoming the challenges in embodied intelligence applications, particularly the scarcity of large-scale, high-quality, and multi-platform compatible robotic operation data [2][5]. Group 1: Challenges in Embodied Intelligence Data - The current embodied intelligence data faces three main challenges: lack of standards, weak quality control, and high usage barriers, which severely restrict industry development [3]. - Existing datasets are often characterized by insufficient real-world coverage, single-task focus, and excessive laboratory conditions, leading to a lack of generalizability across different robotic platforms [2][6]. Group 2: RoboCOIN Dataset Features - RoboCOIN dataset boasts three core advantages: it includes 15 heterogeneous robotic platforms, over 180,000 trajectories, and 421 tasks, making it the most diverse bimanual real-machine dataset globally [5][7]. - The dataset covers 16 types of real-world environments and includes 432 different objects, supporting 36 types of bimanual operation skills, thus creating a progressive task system from simple to complex [7][8]. Group 3: Data Quality and Annotation - The dataset is collected through human teleoperation, ensuring high quality with over 180,000 real trajectories, each equipped with multi-view images, joint states, and end-effector poses, all synchronized in time and unified in coordinate systems [8][9]. - RoboCOIN introduces a "Hierarchical Capability Pyramid" for multi-resolution annotation, enhancing data information density and teaching value, allowing models to learn "what to do," "how to do it," and "how to do it accurately" [10][19]. Group 4: CoRobot Software Framework - To support the efficient construction and application of RoboCOIN, the CoRobot software framework has been developed, featuring three core components: RTML for trajectory markup, an automated annotation toolchain, and a unified multi-embodiment management platform [12][13][16]. - The RTML significantly improves data reliability by automatically evaluating and filtering low-quality trajectories [13]. Group 5: Performance Improvement - Experiments on real robotic platforms show that the introduction of RoboCOIN's hierarchical annotation has increased the success rate of complex tasks from 20% to 70% [19]. - Training models with high-quality data filtered through RTML has resulted in an average success rate improvement of 23%, validating the "quality over quantity" data paradigm [20]. Group 6: Community and Collaboration - The initiative encourages global researchers and developers to join the RoboCOIN community, aiming to build a new ecosystem for embodied data and promote the transition of embodied intelligence from laboratories to various industries [22][23].