{"@context":"https://schema.org","@type":"NewsArticle","generatedAt":"2026-07-23T06:40:50.084Z","headline":"物理 AI 仿真引擎概览：MuJoCo、MuJoCo Warp 与 NVIDIA Isaac Sim/Lab","description":"物理 AI 系统因真实世界数据采集慢、成本高且风险大，转而依赖仿真生成大规模物理数据。MuJoCo 侧重精确动力学与接触建模，其 GPU 加速版 MuJoCo Warp 适合大规模策略训练；NVIDIA Isaac Sim 与 Isaac Lab 则提供 GPU 加速物理与 RTX 渲染，支持合成数据生成与强化学习。","url":"https://www.aioga.com/news/cmrv4quhd00fvbizanl0mc1ui/","mainEntityOfPage":"https://www.aioga.com/news/cmrv4quhd00fvbizanl0mc1ui/","datePublished":"2026-07-21T20:00:27.000Z","dateModified":"2026-07-21T20:00:27.000Z","inLanguage":"zh-CN","publisher":{"@type":"NewsMediaOrganization","name":"Aioga","url":"https://www.aioga.com"},"citation":["https://huggingface.co/blog/nvidia/state-of-simulation-for-physical-ai","https://aihot.virxact.com/items/cmrv4quhd00fvbizanl0mc1ui"],"canonicalUrl":"https://www.aioga.com/news/cmrv4quhd00fvbizanl0mc1ui/","directAnswer":{"@type":"Answer","text":"Aioga 编辑摘要：物理 AI 系统因真实世界数据采集慢、成本高且风险大，转而依赖仿真生成大规模物理数据。 Aioga 将其归入「技巧观点」方向，重点关注它对真实使用和行业竞争的影响。","url":"https://www.aioga.com/news/cmrv4quhd00fvbizanl0mc1ui/","dateCreated":"2026-07-21T20:00:27.000Z","author":{"@type":"Organization","@id":"https://www.aioga.com/authors/aioga-editorial/#editorial-team","name":"Aioga Editorial Team","url":"https://www.aioga.com/authors/aioga-editorial/"}},"evidence":[{"@type":"CreativeWork","name":"huggingface.co source article","url":"https://huggingface.co/blog/nvidia/state-of-simulation-for-physical-ai","datePublished":"2026-07-21T20:00:27.000Z","provider":{"@type":"Organization","name":"huggingface.co","url":"https://huggingface.co/blog/nvidia/state-of-simulation-for-physical-ai"}},{"@type":"CreativeWork","name":"AIHot archive record","url":"https://aihot.virxact.com/items/cmrv4quhd00fvbizanl0mc1ui","datePublished":"2026-07-21T20:00:27.000Z","provider":{"@type":"Organization","name":"AIHot","url":"https://aihot.virxact.com/items/cmrv4quhd00fvbizanl0mc1ui"}}],"aggregationSource":"Hugging Face：Blog（RSS）","originalPublisher":{"name":"huggingface.co","url":"https://huggingface.co/blog/nvidia/state-of-simulation-for-physical-ai"},"article":{"id":"cmrv4quhd00fvbizanl0mc1ui","slug":"cmrv4quhd00fvbizanl0mc1ui","url":"https://www.aioga.com/news/cmrv4quhd00fvbizanl0mc1ui/","title":"物理 AI 仿真引擎概览：MuJoCo、MuJoCo Warp 与 NVIDIA Isaac Sim/Lab","title_en":"The State of Simulation for Physical AI： An Overview","summary":"物理 AI 系统因真实世界数据采集慢、成本高且风险大，转而依赖仿真生成大规模物理数据。MuJoCo 侧重精确动力学与接触建模，其 GPU 加速版 MuJoCo Warp 适合大规模策略训练；NVIDIA Isaac Sim 与 Isaac Lab 则提供 GPU 加速物理与 RTX 渲染，支持合成数据生成与强化学习。","source":"Hugging Face：Blog（RSS）","sourceUrl":"https://huggingface.co/blog/nvidia/state-of-simulation-for-physical-ai","aiHotUrl":"https://aihot.virxact.com/items/cmrv4quhd00fvbizanl0mc1ui","publishedAt":"2026-07-21T20:00:27.000Z","category":"技巧观点","score":33,"selected":false,"articleBody":["：https://cdn-uploads.huggingface.co/production/uploads/6994dc99f850a10f03fd0b21/Mg3Q_CTe3wrJr6GP_T-DF.gif Figure 1 : Humanoid robot locomotion simulation. The robot's pose is represented by tracked body keypoints (green markers), while successive robot instances illustrate its movement through time. Directional arrows indicate commanded motion, demonstrating the use of a physics-based simulation environment for training and evaluating robot locomotion and control policies.","The primary challenge in building physical AI systems is data availability. Large language models (LLMs) and vision-language models (VLMs) can be trained on internet-scale datasets, but robotics and physical AI systems do not have the same advantage. To train a physical AI system, a robot must learn the consequences of interacting with the physical world. For example, it needs to understand what happens when a cup slips, a cable bends, or a gripper contacts an object at the wrong angle.","Collecting this kind of data in the real world is slow, expensive, risky, and sometimes impractical due to the destructive nature of the tasks. Simulation provides a bridge by enabling developers to generate large amounts of photorealistic, physically grounded data. By teleoperating robots in simulation and scaling data collection through GPU parallelism, developers can generate thousands of hours of robot experience at a fraction of the cost of real-world collection.","Earlier robotics simulators were often used primarily to debug geometry, test controllers, or visualize robot motion. Today, simulation has become part of the model development loop. Teams use it to generate perception datasets, train reinforcement learning policies, collect demonstrations, augment real-world data, benchmark models, and test policies against rare or adversarial scenarios. This shift is why industrial research labs and academic groups are increasingly contributing to, or developing, simulation engines that can meet these new requirements.","These requirements can be understood through a three-computer paradigm:","：https://cdn-uploads.huggingface.co/production/uploads/6994dc99f850a10f03fd0b21/DjwalbNws06-13MvI1f4h.png Figure 2 : A physical system (Earth and robot) continuously exchanges data with its virtual representation (digital model), enabling monitoring, analysis, prediction, and control through a bidirectional feedback loop.","Each computer plays a different role depending on the task’s latency, throughput, accuracy, and deployment requirements.","Many simulation engines are available today, each targeting different use cases such as humanoid robots, aerial vehicles, ground robots, autonomous vehicles, quadrupeds, and dexterous manipulators. These engines also differ in their support for reinforcement learning, batched simulation, contact-rich physics, photorealistic rendering, and sensor simulation. Refer to Figure 2 for a categorization of simulation engines by robot domain and acceleration profile.","Developers often struggle with which simulation engine to use for the use case they are tackling. Oftentimes, this falls on a few fundamental questions, such as","These factors would help developers in deciding which simulation engine to use for their development needs.","NVIDIA Isaac Sim：https://developer.nvidia.com/isaac/sim and Isaac Lab：https://developer.nvidia.com/isaac/lab, MuJoCo：https://mujoco.org/, PyBullet：https://pybullet.org/wordpress/, Drake：https://github.com/RobotLocomotion/drake, and Genesis：https://genesis-world.readthedocs.io/en/latest/# are some of the most popular simulation engines for humanoids and two-arm dexterous manipulation. Each of these simulation engines is unique to specific use cases such as scaling reinforcement learning, generating synthetic data, testing algorithms and/or models in simulation. Here we will provide a quick overview of MuJoCo, MuJoCo Warp, NVIDIA Isaac Sim, and Isaac Lab.","MuJoCo：https://github.com/google-deepmind/mujoco, short for Multi-Joint dynamics with Contact, is a fast, accurate, open-source physics engine built mainly for robotics, biomechanics, reinforcement learning, control, and simulation of articulated systems. Unlike engines focused mostly on games or visual realism, MuJoCo is designed around precise dynamics, contact-rich motion, and model-based optimization. MuJoCo emphasizes speed, accuracy, and modeling power for systems where physical correctness matters.","：https://cdn-uploads.huggingface.co/production/uploads/6994dc99f850a10f03fd0b21/2l7V9gfY-F42p4pw7ita4.jpeg Figure 3 : Robotic manipulator simulated in MuJoCo. The figure illustrates a multi-joint robotic arm with a parallel gripper operating in a physics-based simulation environment. MuJoCo is used to model the robot's dynamics, kinematics, and contact interactions, enabling the development and evaluation of motion planning, manipulation, and control algorithms prior to real-world deployment.","While MuJoCo is not always the best choice for photorealistic rendering or massive GPU-parallel simulation, but some of the core features that MuJoCo is well known for are a deterministic pipeline, well-defined inverse dynamics with contacts, strong contact modeling, and generalized-coordinate simulation.","MuJoCo Warp：https://github.com/google-deepmind/mujoco_warp, also called MJWarp, is a GPU-accelerated implementation of MuJoCo written in NVIDIA Warp, a Python framework for writing high-performance CUDA-accelerated differentiable kernels - tailored for robot learning and physics simulation.","It brings MuJoCo-style articulated-body physics, contact-rich simulation, and robot dynamics into a batched GPU setting, making it well suited for reinforcement learning and large-scale policy training. Compared with classic CPU MuJoCo, MJWarp is aimed more at throughput than single-step latency: it is designed to simulate many worlds in parallel, reduce CPU-GPU transfer bottlenecks, and scale contact-heavy robot tasks more efficiently for learning workloads.","Isaac Sim：https://github.com/isaac-sim/IsaacSim is an open-source robotics simulation framework built on NVIDIA Omniverse. It uses OpenUSD：https://developer.nvidia.com/openusd as its core scene and data layer, allowing robots, environments, sensors, materials, lighting, and physics properties to be represented as USD prims and schemas. Isaac Sim provides high-fidelity physics through PhysX, photorealistic RTX rendering, and robotics-focused sensor simulation for cameras, depth, lidar, radar, segmentation, and synthetic data generation workflows. It can ingest assets from CAD, URDF, MJCF, USD, and real-world reconstruction pipelines, enabling robots to be tested in realistic digital-twin environments.","NVIDIA Isaac Lab 3.0：https://isaac-sim.github.io/IsaacLab/release/3.0.0-beta2/index.html is an open-source, GPU-accelerated, agent-ready simulation framework for robot learning, designed to train and evaluate robot policies at scale. This direction supports agent-assisted workflows for building environments, tuning physics, debugging, profiling, sim-to-sim transfer, and sim-to-real deployment. Its modular manager-based and direct workflows support reinforcement learning, imitation learning, motion planning, data collection, and policy evaluation.","With Isaac Lab 3.0.0 - We have decoupled the Isaac Sim and omniverse dependency, and made Isaac Lab a lightweight, multi backend Robot learning framework.","The new architecture separates backend-specific code from the core Isaac Lab API. Developers can select to use Isaac Sim with PhysX and RTX rendering for photorealistic, sensor-rich workflows, or run light-weightheadless Newton physics for high-throughput simulation. Additionally, developers can add photoreal sensors via the standalone OVRTX renderer：https://github.com/nvidia-omniverse/ovrtx, or leverage the Newton renderer for vision-based RL tasks that require a very large number of environments.","Modern robotics simulation frameworks are powered by numerical physics: rigid-body and soft-body dynamics, contact and friction models, joint constraints, actuators, sensors, and time integration. At each simulation step, the engine estimates how bodies move under forces, torques, collisions, gravity, constraints, and controller inputs. For robot learning, this physics must be fast, stable, repeatable, and scalable enough to run thousands of parallel environments, while also being accurate enough to transfer learned behavior from simulation to real robots. To address these, the Newton library is created.","：https://cdn-uploads.huggingface.co/production/uploads/6994dc99f850a10f03fd0b21/tXtjQgKuWSqBf495zdgfB.png Figure 4 : The Newton Physics Engine is an open-source, GPU-accelerated simulation engine built upon NVIDIA Warp, designed for roboticists and simulation researchers.","PyBullet：https://github.com/bulletphysics/bullet3 remains a useful CPU baseline for quick prototyping; DART：https://github.com/dartsim/dart and ODE：https://github.com/thomasmarsh/ODE are still Gazebo back-ends; Drake：https://drake.mit.edu/ is the gold standard if you need contact-implicit trajectory optimisation and rigorous numerics rather than throughput. Each has a niche. None of them is the answer to “I need 4,096 humanoids on one GPU.”","Robot simulation has quietly become one of the most important layers in the modern embodied AI stack. As models move from static datasets into physical interaction, the ability to generate high-quality, scalable, and diverse simulated experiences is no longer optional, it is foundational. Simulation is where policies are stress-tested, where data is amplified, and increasingly, where entire training pipelines begin.","For a long time, the robot-simulator conversation has focused on \"which engine performs the fastest\" and for many workloads this statement still holds true. What we find more interesting in 2026 is a different question: which pieces of the stack are likely to become the shared infrastructure that different engines would build on top of? There is no definite answer out there yet but a few candidates seem plausible from where we sit. The ecosystem is fragmenting and recomposing into a layered stack, where different tools specialize, interoperate, and sometimes compete to define the abstractions others will build on.","What we do feel confident saying is that an unusual amount of this work is being done in the open-source, openly governed, and available to anyone with a consumer GPU. A growing share of the core infrastructure physics engines, differentiable simulators, and training pipelines is emerging in the open, with open-source projects, open governance, and GPU-accessible tooling lowering the barrier to entry and accelerating iteration. That alone is worth celebrating, regardless of which engines you end up reaching for.","Looking Ahead: From Concepts to Practice","This article takes a step back to map that landscape in 2026: what exists, how the major engines relate, and why these shifts matter for anyone building in Physical AI. The next article in this series goes one layer deeper and looks at Warp and MuJoCo Warp itself, what it is, and how to drive it. From there, we will work our way up, and by the end of the series we will have a practical end-to-end robotics example, trained on a GPU using a software stack that did not exist two years ago.","If you build something with the tools described here, whichever ones you pick, we would love to see it. Open an issue on any of the linked repositories, or reach out to us on Discord Nvidia Omniverse：https://discord.com/invite/nvidiaomniverse."],"articleImages":[{"sourceUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/6515c41e4256270d6fa09259/O43dQxNqwcQ6ONnr_dDGt.jpeg","alt":"","afterParagraph":0,"url":"/media/articles/cmrv4quhd00fvbizanl0mc1ui/9cc39ecd23cb78c6.webp"},{"sourceUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/65e485b19fb58a5115bdf037/1GT6xRNPw_MF5qxinLDEZ.jpeg","alt":"","afterParagraph":0,"url":"/media/articles/cmrv4quhd00fvbizanl0mc1ui/86a12111e98800e8.webp"}],"mediaStatus":"ok","articleBodyZh":["：https://cdn-uploads.huggingface.co/production/uploads/6994dc99f850a10f03fd0b21/Mg3Q_CTe3wrJr6GP_T-DF.gif 图 1：类人机器人运动仿真。机器人的姿态由跟踪的身体关键点（绿色标记）表示，而连续的机器人实例展示了其随时间的运动。方向箭头表示指令运动，展示了基于物理的仿真环境在训练和评估机器人运动与控制策略中的应用。","构建物理 AI 系统的主要挑战是数据可用性。大型语言模型（LLMs）和视觉-语言模型（VLMs）可以在互联网规模的数据集上进行训练，但机器人学和物理 AI 系统没有同样的优势。要训练物理 AI 系统，机器人必须学习与物理世界交互的后果。例如，它需要理解当杯子滑落、电缆弯曲或抓手以错误角度接触物体时会发生什么。","在现实世界中收集这种数据速度慢、成本高、风险大，有时由于任务的破坏性甚至不切实际。仿真提供了一座桥梁，使开发者能够生成大量逼真、物理基础的数据。通过在仿真中进行远程操作机器人，并通过 GPU 并行加速数据收集，开发者可以以远低于现实世界收集成本的方式生成数千小时的机器人经验。","早期的机器人仿真器通常主要用于调试几何结构、测试控制器或可视化机器人运动。如今，仿真已成为模型开发循环的一部分。团队使用它生成感知数据集、训练强化学习策略、收集示范、增强现实世界数据、进行模型基准测试，以及在罕见或对抗性场景下测试策略。这种转变是工业研究实验室和学术团队越来越多地贡献或开发能够满足这些新需求的仿真引擎的原因。","这些需求可以通过三计算机范式来理解：","：https://cdn-uploads.huggingface.co/production/uploads/6994dc99f850a10f03fd0b21/DjwalbNws06-13MvI1f4h.png 图 2：一个物理系统（地球和机器人）与其虚拟表示（数字模型）持续交换数据，通过双向反馈循环实现监控、分析、预测和控制。","每台计算机根据任务的延迟、吞吐量、精度和部署需求扮演不同的角色。","如今有许多模拟引擎可供使用，每个引擎针对不同的用例，例如类人机器人、空中飞行器、地面机器人、自动驾驶车辆、四足机器人和灵巧操控器。这些引擎在强化学习支持、批量模拟、富接触物理、真实感渲染和传感器模拟方面也有所不同。参见图 2，根据机器人领域和加速特性对模拟引擎进行分类。","开发者常常难以决定在他们所处理的用例中使用哪种模拟引擎。通常，这归结为一些基本问题，例如","这些因素将帮助开发者决定为其开发需求使用哪种模拟引擎。","NVIDIA Isaac Sim：https://developer.nvidia.com/isaac/sim 和 Isaac Lab：https://developer.nvidia.com/isaac/lab，MuJoCo：https://mujoco.org/，PyBullet：https://pybullet.org/wordpress/，Drake：https://github.com/RobotLocomotion/drake，以及 Genesis：https://genesis-world.readthedocs.io/en/latest/# 是一些最受欢迎的类人和双臂灵巧操作模拟引擎。每个模拟引擎都针对特定用例，例如扩展强化学习、生成合成数据、在模拟中测试算法和/或模型。这里我们将简要概述 MuJoCo、MuJoCo Warp、NVIDIA Isaac Sim 和 Isaac Lab。","MuJoCo：https://github.com/google-deepmind/mujoco，全称为多关节接触动力学，是一个快速、精确的开源物理引擎，主要用于机器人技术、生物力学、强化学习、控制以及关节系统的仿真。与主要针对游戏或视觉逼真度的引擎不同，MuJoCo的设计围绕精确动力学、丰富接触的运动以及基于模型的优化。MuJoCo强调速度、精度和建模能力，适用于物理正确性至关重要的系统。","：https://cdn-uploads.huggingface.co/production/uploads/6994dc99f850a10f03fd0b21/2l7V9gfY-F42p4pw7ita4.jpeg 图3：在MuJoCo中模拟的机器人操纵器。该图展示了一个在物理仿真环境中操作的多关节机械臂及平行夹具。MuJoCo用于建模机器人动力学、运动学及接触交互，使开发和评估运动规划、操作和控制算法在实际部署之前成为可能。","虽然MuJoCo在照片级渲染或大规模GPU并行仿真方面不总是最佳选择，但MuJoCo的一些核心特点是确定性流程、定义良好的带接触的逆动力学、强大的接触建模以及广义坐标仿真。","MuJoCo Warp：https://github.com/google-deepmind/mujoco_warp，也称为MJWarp，是MuJoCo的GPU加速实现，采用NVIDIA Warp编写，这是一个用于编写高性能CUDA加速可微核的Python框架——专为机器人学习和物理仿真设计。","它将MuJoCo风格的关节身体物理、丰富接触的仿真和机器人动力学引入批量GPU环境，非常适合强化学习和大规模策略训练。与经典CPU版MuJoCo相比，MJWarp更注重吞吐量而非单步延迟：它旨在并行模拟多个世界，减少CPU-GPU传输瓶颈，并更高效地扩展重接触机器人任务以适应学习工作负载。","Isaac Sim：https://github.com/isaac-sim/IsaacSim 是一个基于 NVIDIA Omniverse 构建的开源机器人仿真框架。它使用 OpenUSD：https://developer.nvidia.com/openusd 作为其核心场景和数据层，允许将机器人、环境、传感器、材质、光照和物理属性表示为 USD 原语和模式。Isaac Sim 通过 PhysX 提供高保真物理模拟，支持真实感的 RTX 渲染，以及面向机器人的传感器仿真，包括相机、深度、激光雷达、雷达、分割以及合成数据生成工作流。它可以从 CAD、URDF、MJCF、USD 以及真实世界重建管道中导入资产，使机器人能够在逼真的数字孪生环境中进行测试。","NVIDIA Isaac Lab 3.0：https://isaac-sim.github.io/IsaacLab/release/3.0.0-beta2/index.html 是一个开源、GPU 加速、面向智能体的机器人学习仿真框架，旨在大规模训练和评估机器人策略。这个方向支持用于创建环境、调试物理、调试、性能分析、仿真到仿真迁移以及仿真到现实部署的智能体辅助工作流。其模块化的基于管理器和直接的工作流支持强化学习、模仿学习、运动规划、数据收集和策略评估。","在 Isaac Lab 3.0.0 中——我们已经将 Isaac Sim 与 Omniverse 的依赖解耦，并使 Isaac Lab 成为一个轻量级、多后端的机器人学习框架。","新的架构将后端特定代码与核心 Isaac Lab API 分离。开发者可以选择使用带有 PhysX 和 RTX 渲染的 Isaac Sim 进行真实感、传感器丰富的工作流，或者运行轻量级无头的 Newton 物理引擎进行高吞吐量仿真。此外，开发者可以通过独立的 OVRTX 渲染器：https://github.com/nvidia-omniverse/ovrtx 添加真实感传感器，或利用 Newton 渲染器进行需要大量环境的基于视觉的强化学习任务。","现代机器人仿真框架依赖数值物理：刚体和软体动力学、接触与摩擦模型、关节约束、执行器、传感器以及时间积分。在每个仿真步骤中，引擎都会估算物体在力、力矩、碰撞、重力、约束和控制器输入下的运动情况。对于机器人学习而言，这种物理仿真必须足够快速、稳定、可重复，并且具有可扩展性，以运行成千上万个并行环境，同时也必须足够精确，以便将模拟中学到的行为迁移到真实机器人上。为了解决这些问题，Newton 库应运而生。","：https://cdn-uploads.huggingface.co/production/uploads/6994dc99f850a10f03fd0b21/tXtjQgKuWSqBf495zdgfB.png 图 4：Newton 物理引擎是一个开源 GPU 加速的仿真引擎，基于 NVIDIA Warp 构建，专为机器人学家和仿真研究人员设计。","PyBullet：https://github.com/bulletphysics/bullet3 仍然是快速原型开发的有用 CPU 基准；DART：https://github.com/dartsim/dart 和 ODE：https://github.com/thomasmarsh/ODE 仍然是 Gazebo 的后端；Drake：https://drake.mit.edu/ 如果你需要接触隐式轨迹优化和严格的数值计算而不是吞吐量，则是黄金标准。每个都有其特定用途。没有一个能满足“我需要在一块 GPU 上同时运行 4,096 个类人机器人”的需求。","机器人仿真悄然成为现代具身 AI 堆栈中最重要的层之一。随着模型从静态数据集转向物理交互，生成高质量、可扩展且多样的仿真体验的能力已不再是可选项，而是基础性要求。仿真是策略受压测试的地方，是数据增强的场所，并且越来越多地成为整个训练流程的起点。","长期以来，机器人模拟器的讨论一直聚焦于“哪个引擎速度最快”，对于许多工作负载来说，这一说法仍然成立。我们在2026年发现更有趣的问题是：堆栈中的哪些部分可能成为不同引擎建立在其上的共享基础设施？目前还没有明确的答案，但从我们的位置来看，有几个候选者似乎是合理的。生态系统正在分化并重组为分层堆栈，不同的工具在其中各自专长、互操作，有时还会竞争以定义其他工具将基于其构建的抽象。","我们可以自信地说，有大量此类工作正在开源、公开治理，并可供任何拥有消费者级GPU的人使用。核心基础设施的一部分——物理引擎、可微分模拟器和训练管道——正在以开源形式出现，开源项目、公开治理和可通过GPU访问的工具降低了入门门槛，加快了迭代速度。仅这一点就值得庆祝，无论你最终选择使用哪个引擎。","展望：从概念到实践","本文退一步，描绘了2026年的这一领域景观：现有的内容是什么，主要引擎如何关联，以及这些变化为什么对任何在物理AI领域构建的人都至关重要。本系列的下一篇文章将深入一层，探讨Warp和MuJoCo Warp本身，它是什么，以及如何驱动它。从那里，我们将逐步向上推进，到系列结尾，我们将展示一个实践性的端到端机器人示例，使用两年前尚不存在的软件堆栈，在GPU上进行训练。","如果你使用这里描述的工具构建了某些东西，无论你选择哪一个，我们都很希望看到。可以在任何一个链接的仓库中开启问题，或者通过Discord Nvidia Omniverse联系我们：https://discord.com/invite/nvidiaomniverse"],"translationStatus":"translated","bodyOrigin":"source-page","editorial":{"summary":"Aioga 编辑摘要：物理 AI 系统因真实世界数据采集慢、成本高且风险大，转而依赖仿真生成大规模物理数据。 Aioga 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