Core Viewpoint - The article discusses the ongoing debate in the field of embodied intelligence regarding the reliance on simulation efficiency versus real-world data, and the potential of world models to redefine the landscape of data utilization in this domain [4][8]. Group 1: Understanding Sim-to-Real Gap - The "Sim-to-Real gap" refers to the discrepancies between simulated environments and real-world scenarios, primarily due to incomplete simulations that fail to accurately replicate visual and physical details [8]. - Research indicates that the gap exists because simulation models do not fully capture the complexities of the real world, leading to limited generalization capabilities and a focus on specific scenarios [8][11]. - Solutions to bridge this gap involve optimizing data, including designing virtual and real data ratios and leveraging AIGC to generate diverse datasets that balance volume and authenticity [11][12]. Group 2: Data Utilization in Embodied Intelligence - There is a consensus among experts that while real data is ideal for training, the current landscape necessitates a reliance on simulation data due to the scarcity of high-quality real-world datasets in the embodied intelligence field [20][21]. - Simulation data plays a crucial role in foundational model iteration and testing, as it allows for safe and efficient algorithm testing before deploying on real machines [21][24]. - The potential of simulation in scaling reinforcement learning is highlighted, as well-constructed simulators can facilitate large-scale parallel training, enabling models to learn from scenarios that are difficult to capture in real life [24][26]. Group 3: World Models and Future Directions - The article emphasizes the significance of world models in future research, particularly in areas like autonomous driving and embodied intelligence, showcasing their potential in general visual understanding and long-term planning [30][32]. - Challenges remain in automating the generation of simulation data and ensuring the diversity and generalization of actions within simulations, which are critical for advancing the field [28][29]. - The introduction of new modalities, such as force and touch, into world models is suggested as a promising direction for future research, despite current limitations in computational resources [30][31]. Group 4: Reaction to Boston Dynamics Technology - Experts acknowledge the advanced capabilities of Boston Dynamics robots, particularly their smooth execution of complex tasks that require sophisticated motion control [33][37]. - The discussion highlights the importance of hardware and data in the field of embodied intelligence, with Boston Dynamics' approach serving as a benchmark for future developments [37][39]. - The consensus is that the seamless performance of these robots is attributed not only to hardware differences but also to superior motion control techniques that could inform future research in embodied intelligence [39][41].

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].