GPU加速的多模态机器人仿真平台，支持大规模学习与真实感训练
Isaac Lab: A GPU-Accelerated Simulation Framework for Multi-Modal Robot Learning
- 基于GPU并行物理与渲染，构建可组合的仿真环境
- 集成传感器模拟与数据管道，支持强化与模仿学习
- 适合研究复杂操控与跨体感移动的机器人团队
我们提出 Isaac Lab，作为 Isaac Gym 的自然演进，将原生 GPU 机器人仿真推进至大规模多模态学习时代。该框架融合高保真 GPU 并行物理、照片级渲染，以及模块化、可组合的环境设计与策略训练架构。除物理与渲染外，还整合了执行器模型、多频传感器模拟、数据采集流水线和领域随机化工具，统一了大规模强化与模仿学习的最佳实践。其应用涵盖全身控制、跨体感移动、接触丰富与灵巧操作，以及人类示范驱动的技能获取。最后，我们展望与可微分、GPU加速的 Newton 物理引擎的集成，有望开启可扩展、数据高效且基于梯度的机器人学习新路径。我们相信，Isaac Lab 凭借先进的仿真能力、丰富的感知模拟与数据中心级执行，将推动机器人研究迎来下一代突破。
原文摘要 · Abstract (English)
We present Isaac Lab, the natural successor to Isaac Gym, which extends the paradigm of GPU-native robotics simulation into the era of large-scale multi-modal learning. Isaac Lab combines high-fidelity GPU parallel physics, photorealistic rendering, and a modular, composable architecture for designing environments and training robot policies. Beyond physics and rendering, the framework integrates actuator models, multi-frequency sensor simulation, data collection pipelines, and domain randomization tools, unifying best practices for reinforcement and imitation learning at scale within a single extensible platform. We highlight its application to a diverse set of challenges, including whole-body control, cross-embodiment mobility, contact-rich and dexterous manipulation, and the integration of human demonstrations for skill acquisition. Finally, we discuss upcoming integration with the differentiable, GPU-accelerated Newton physics engine, which promises new opportunities for scalable, data-efficient, and gradient-based approaches to robot learning. We believe Isaac Lab's combination of advanced simulation capabilities, rich sensing, and data-center scale execution will help unlock the next generation of breakthroughs in robotics research.
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