Core Viewpoint - The article discusses the release of RoboTwin 2.0, a scalable data generator and benchmark for robust bimanual robotic manipulation, highlighting its advancements over the previous version, RoboTwin 1.0, and its applications in dual-arm collaboration tasks [5][34]. Group 1: Introduction and Background - RoboTwin 2.0 is developed by researchers from Shanghai Jiao Tong University and the University of Hong Kong, focusing on overcoming limitations in data collection and simulation for dual-arm robotic operations [6][8]. - The RoboTwin series has received recognition in major conferences, including CVPR and ECCV, and has been utilized in various competitions [3][9]. Group 2: Features of RoboTwin 2.0 - RoboTwin 2.0 introduces a large-scale domain randomization data synthesis framework, which includes a dataset of 731 instances across 147 object categories, enhancing the robustness of models in unseen environments [8][12]. - The system employs a more user-friendly API for expert code generation, significantly lowering the barrier for utilizing large multimodal models [10][34]. Group 3: Domain Randomization Strategies - The article outlines five key dimensions of domain randomization implemented in RoboTwin 2.0, including scene clutter, background textures, lighting conditions, tabletop heights, and diverse language instructions [16][18][20][21][22]. - These strategies aim to improve the model's adaptability and performance in real-world scenarios by exposing it to a wide variety of training conditions [16][34]. Group 4: Performance Metrics - RoboTwin 2.0 shows significant improvements in performance metrics compared to RoboTwin 1.0, with an average success rate (ASR) increase from 47.4% to 62.1% in typical tasks, and further enhancements with structured feedback [26][27]. - The adaptive grasping capabilities of RoboTwin 2.0 also demonstrate an average success rate improvement of 8.3% across five robotic platforms [28]. Group 5: Real-World Application and Transferability - The system exhibits strong zero-shot transfer capabilities, achieving notable success rates in unseen tasks and complex environments, indicating its potential for real-world applications [31][33]. - The results highlight RoboTwin 2.0's comprehensive advantages in code generation, grasping expansion, environmental robustness, and sim-to-real transfer, providing a solid foundation for future dual-arm operation research [34].