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。
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。
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。
：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.
情报判断
Aioga 编辑摘要
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。
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