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Automating Multi-Bag Testing Loops in Isaac Sim
Learn how to run automated simulation loops using multiple ROS 2 bag files in Isaac Sim for reproducible and scalable robot testing.
Robotic testing often requires repeating the same task under varying conditions—like different sensor logs, controller behaviors, or environmental maps—to uncover edge cases and validate reliability. In Isaac Sim, this process becomes highly scalable through automated replay of multiple ROS 2 bag files. Each bag file acts as a snapshot of real-world input—LiDAR scans, camera feeds, odometry, or joint commands—that can be deterministically replayed within a high-fidelity USD-based simulation.
Using Isaac’s ROS bridge and Python scripting APIs, developers can create testing loops that load one bag after another into the same simulation scene or a series of USD variant environments. This allows for regression testing, controller tuning, and perception benchmarking—all without physically re-running robots in the field. Sensor topics stream back into Isaac Sim in real time, enabling the simulated robot to behave as though it were operating under the original real-world input, while still enabling fine-grained observation, logging, and scenario manipulation.
How it works:
- You load each
.bagfile into the simulation environment using the Isaac ROS bridge. - Use a script or launch file to automate sequential or parallel replay across bags.
- Monitor performance, accuracy, or failure modes between runs to benchmark behaviors.
- Logs from each replay can be stored, tagged, and compared—ideal for regression tests or safety validation.
Where Champion helps:
Managing dozens—or hundreds—of bag file replays manually can quickly become a bottleneck. That’s where Champion steps in. ⚙️
Champion automates the orchestration of ROS 2 bag loops 👜🔁 and links them seamlessly with USD-driven simulation scenes in Isaac Sim 🎮📦. Whether you're testing different controller policies, perception stacks, or environmental variations, Champion ensures each bag is paired with the right scene and robot configuration—all without constant human intervention. 🧠🤖
You get:
- 🔄 Scalable, repeatable test runs
- 🧩 Integrated control over variant scenes, robot models, and sensor setups
- 🧪 Structured validation pipelines with full traceability
- 📝 Consistent logs and test reports across all permutations
It’s like having a robotic QA engineer in the loop—running 24/7. Perfect for continuous validation, regression testing, and simulation-driven development at scale. 🚀
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