Platform / Synthetic Data

    Synthetic Data Generation

    Generate training data using NVIDIA Isaac Sim and Isaac Lab with domain randomization and ground truth labeling for perception and control models.

    Domain Randomization

    Configurable variation of lighting, textures, object placement, and scene parameters using Isaac Sim.

    Ground Truth Labels

    Automatic annotation for segmentation, depth, pose, and bounding boxes with Isaac Lab.

    Scene Generation

    Procedural scene composition with controllable complexity and diversity.

    Training Integration

    Direct pipelines from synthetic data generation to model training workflows.

    Training Use Cases

    Generate labeled datasets with configurable parameters and perfect ground truth for perception and control tasks.

    Perception model training
    Semantic segmentation
    Object detection
    Depth estimation
    Policy pre-training
    Data augmentation
    Perception and vision training workflow
    Control and policy training network
    Reinforcement learning training loop
    Data augmentation and transformation pipeline
    Configurable
    Scene Parameters
    Integrated
    Training Pipelines
    Reproducible
    Data Generation
    Scalable
    Compute Resources