穆尧团队最新！RoboTwin 2.0：用于鲁棒双臂操作的可扩展数据基准

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].