Core Viewpoint - The article discusses the challenges and advancements in training dual-arm robots for complex tasks, emphasizing the need for efficient data collection and simulation methods to enhance their operational capabilities [2]. Group 1: Challenges in Dual-Arm Robot Training - Dual-arm robots play a crucial role in collaborative assembly, tool usage, and object handover in complex scenarios, but training them to perform general operations like VLA faces multiple bottlenecks [2]. - The cost and time required to scale up the collection of real demonstration data are high, making it difficult to cover a wide range of tasks, object shapes, and hardware variations [2]. - Existing simulation methods lack efficient and scalable expert data generation techniques for new tasks, and their domain randomization designs are too superficial to accurately simulate the complexities of real environments [2]. Group 2: Advancements and Solutions - The article highlights the introduction of UniVLA, which efficiently utilizes multi-source heterogeneous data to construct a general and scalable action space for robots [5]. - The CVPR champion solution, BridgeVLA, reportedly improves real machine performance by 32%, showcasing advancements in robot navigation and motion control in real-world scenarios [4].