Fast experiment loops for robotics: simulation, evaluation, and dataset generation

Robotics teams often run large numbers of experiments: simulation rollouts, synthetic dataset generation, and model evaluation runs. These workloads are usually parallel, but running them often requires complex infrastructure, cluster setup, or long waits for shared compute.

In this workshop, we’ll show a simpler approach: running robotics experiments as containerized GPU jobs on Nebius AI Cloud. You’ll see how simulation workloads, evaluation runs, and dataset generation pipelines can be launched directly from a script with automatic GPU provisioning and no cluster configuration.

You’ll get practical patterns you can reuse, see real robotics workflows running step by step, and learn how to structure experiments for faster iteration and reproducibility.

What you’ll learn

• How to run simulation rollouts as repeatable batch jobs
• How to launch parallel experiment sweeps across environments or model checkpoints
• How to generate synthetic datasets from simulation pipelines
• How to collect trajectories, metrics, and logs from experiments
• How to structure robotics workloads for reproducibility and faster experiment loops

We’ll also demonstrate how robotics experiments can run and scale in practice, and how to debug failed runs without rebuilding infrastructure.

Who should attend
Built for robotics engineers, ML researchers, and autonomy teams running simulation experiments, reinforcement learning training, perception model evaluation, or synthetic data generation.