Isaac Lab paper out on arXiv
Our paper Isaac Lab: A GPU-Accelerated Simulation Framework for Multi-Modal Robot Learning is now available on arXiv.
Isaac Lab unifies reinforcement learning, imitation learning, and motion planning workflows across diverse robotic platforms, enabling large-scale sim-to-real research. I contributed as a core developer of the framework alongside Mayank Mittal, James Tigue, Antoine Richard, Octi Zhang, and collaborators.
Pascal Roth
Ph.D. in Robot Learning & Software Engineer
Research on learning-based navigation and autonomy for legged robots, with focus on perceptive planning and sim-to-real transfer.