Champion Use Case

Autonomous systems rely on precise perception and motion strategies, often coordinated through ROS 2. But developing the AI agents that power these systems—especially for real-time tracking or quality prediction—requires scalable data pipelines and high-fidelity synthetic training environments.

🛠️ Challenges:

• ROS nodes are often hand-coded and brittle.
• Simulation environments like Gazebo lack the fidelity to train AI models.
• Seam tracking models need vast amounts of labeled data, which is costly to collect in the real world.
• Integrating LLMs or vision foundation models (e.g. for semantic segmentation or anomaly detection) into this workflow is non-trivial.

🚀 Solution Stack

Champion focuses on the data preparation, simulation automation, and model training stack—enabling teams to build smarter vision models and control agents using NVIDIA’s GPU-native services:

🧱 1. Scene Automation with Isaac Sim

• Champion ingests CAD + URDF + sensor definitions.
• Converts scenes to USD, adds physics materials, lighting, and camera setups.
• Supports parameter sweeps and domain randomization for high-diversity synthetic data.

🔄 2. Data Loop for Vision + LLM Models

• Generate labeled image sequences (bounding boxes, masks, joint states) via Isaac Sim.
• Fine-tune models using NVIDIA TAO or custom PyTorch + CUDA pipelines.