| Title: |
Isaac Lab: A GPU-Accelerated Simulation Framework for Multi-Modal Robot Learning |
| Authors: |
NVIDIA; Mittal, Mayank; Roth, Pascal; Tigue, James; Richard, Antoine; Zhang, Octi; Du, Peter; Serrano-Muñoz, Antonio; Yao, Xinjie; Zurbrügg, René; Rudin, Nikita; Wawrzyniak, Lukasz; Rakhsha, Milad; Denzler, Alain; Heiden, Eric; Borovicka, Ales; Ahmed, Ossama; Akinola, Iretiayo; Anwar, Abrar; Carlson, Mark T.; Feng, Ji Yuan; Garg, Animesh; Gasoto, Renato; Gulich, Lionel; Guo, Yijie; Gussert, M.; Hansen, Alex; Kulkarni, Mihir; Li, Chenran; Liu, Wei; Makoviychuk, Viktor; Malczyk, Grzegorz; Mazhar, Hammad; Moghani, Masoud; Murali, Adithyavairavan; Noseworthy, Michael; Poddubny, Alexander; Ratliff, Nathan; Rehberg, Welf; Schwarke, Clemens; Singh, Ritvik; Smith, James Latham; Tang, Bingjie; Thaker, Ruchik; Trepte, Matthew; Van Wyk, Karl; Yu, Fangzhou; Millane, Alex; Ramasamy, Vikram; Steiner, Remo; Subramanian, Sangeeta; Volk, Clemens; Chen, CY; Jawale, Neel; Kuruttukulam, Ashwin Varghese; Lin, Michael A.; Mandlekar, Ajay; Patzwaldt, Karsten; Welsh, John; Zhao, Huihua; Anes, Fatima; Lafleche, Jean-Francois; Moënne-Loccoz, Nicolas; Park, Soowan; Stepinski, Rob; Van Gelder, Dirk; Amevor, Chris; Carius, Jan; Chang, Jumyung; Chen, Anka He; Ciechomski, Pablo de Heras; Daviet, Gilles; Mohajerani, Mohammad; von Muralt, Julia; Reutskyy, Viktor; Sauter, Michael; Schirm, Simon; Shi, Eric L.; Terdiman, Pierre; Vilella, Kenny; Widmer, Tobias; Yeoman, Gordon; Chen, Tiffany; Grizan, Sergey; Li, Cathy; Li, Lotus; Smith, Connor; Wiltz, Rafael; Alexis, Kostas; Chang, Yan; Chu, David; Fan, Linxi "Jim"; Farshidian, Farbod; Handa, Ankur; Huang, Spencer; Hutter, Marco; Narang, Yashraj; Pouya, Soha; Sheng, Shiwei; Zhu, Yuke; Macklin, Miles; Moravanszky, Adam; Reist, Philipp; Guo, Yunrong; Hoeller, David; State, Gavriel |
| Publication Year: |
2025 |
| Collection: |
ArXiv.org (Cornell University Library) |
| Subject Terms: |
Robotics; Artificial Intelligence |
| Description: |
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. ; Code and documentation are available here: https://github.com/isaac-sim/IsaacLab |
| Document Type: |
text |
| Language: |
unknown |
| Relation: |
http://arxiv.org/abs/2511.04831 |
| Availability: |
http://arxiv.org/abs/2511.04831 |
| Accession Number: |
edsbas.2A55E625 |
| Database: |
BASE |