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].

Core Insights - The article discusses the development of RoboTwin 2.0, a scalable data generation framework aimed at enhancing bimanual robotic manipulation through robust domain randomization and automated expert data generation [2][6][18]. Group 1: Motivation and Challenges - Existing synthetic datasets for bimanual robotic manipulation are insufficient, facing challenges such as lack of efficient data generation methods for new tasks and overly simplified simulation environments [2][5]. - RoboTwin 2.0 addresses these challenges by providing a scalable simulation framework that supports automatic, large-scale generation of diverse and realistic data [2][6]. Group 2: Key Components of RoboTwin 2.0 - RoboTwin 2.0 integrates three key components: an automated expert data generation pipeline, comprehensive domain randomization, and entity-aware adaptation for diverse robotic platforms [6][18]. - The automated expert data generation pipeline utilizes multimodal large language models (MLLMs) and simulation feedback to iteratively optimize task execution code [10][12]. Group 3: Domain Randomization - Domain randomization is applied across five dimensions: clutter, background texture, lighting conditions, desktop height, and diverse language instructions, enhancing the robustness of strategies against environmental variability [12][13]. - The framework generates a large object library (RoboTwin-OD) with 731 instances across 147 categories, each annotated with semantic and operational labels [3][18]. Group 4: Data Collection and Benchmarking - Over 100,000 dual-arm operation trajectories were collected across 50 tasks, supporting extensive benchmarking and evaluation of robotic strategies [24][22]. - The framework allows for flexible entity configurations, ensuring compatibility with diverse hardware setups and promoting scalability for future robotic platforms [20][22]. Group 5: Experimental Analysis - Evaluations demonstrated that RoboTwin 2.0 significantly improves the success rates of robotic tasks, particularly for low-degree-of-freedom platforms, with average increases of 8.3% in task success rates [29][31]. - The framework's data enhances the generalization capabilities of models, showing substantial improvements in performance when tested in unseen scenarios [32][34].