以下文章来源于具身智能之心 ，作者具身智能之心 具身智能之心 . 与世界交互，更进一步 点击下方 卡片 ，关注" 具身智能 之心 "公众号 编辑丨具身智能之心 本文只做学术分享，如有侵权，联系删文 然而Physical Intelligence (PI)联合创始人、具身智能领域的先行者Sergey Levine始终坚称：替代数据是叉勺（叉子勺子二合一的产物，既不 如勺子，也不如叉子），真实交互数据不可替代——这究竟是策略局限，还是数据本质的铁律？如今，Genie3携世界模型横空出世，能够 从文本生成可交互的动态环境，甚至驱动在线规划。这是否意味着我们正站在"仿真"与"现实"二元对立终结的前夜？世界模型会成为数据 问题的终极答案，还是仅仅换了一种形式的sim，并依然难逃Sim-to-Real gap的宿命？ 本场技术圆桌，我们邀请到国内Sim2Real领域四位杰出青年科学家—— 与他们四位共话前沿，从高保真3D资产构建、神经渲染的物理瓶颈、铰链体结构优化，到VLA模型的解耦设计等方面入手深入探讨：具身 智能的数据之路，究竟通向仿真、现实，还是那个正在觉醒的"世界模型"？ 智驾的学术领袖和未来的具身学术领袖，Un ...

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].