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Simulation & Robotics SIM-003 2024

ML Training Simulation Environments

Synthetic data generation and reinforcement learning sandboxes

The problem

Training ML models for physical-world tasks requires enormous labeled datasets. Collecting real-world data is slow, expensive, and limited in scenario diversity.

The approach

Built configurable environments that procedurally generate diverse training scenarios with pixel-perfect ground-truth labels. Domain randomization improved sim-to-real transfer.

Outcomes
  • Millions of labeled samples generated without manual annotation
  • Domain randomization improved real-world generalization
  • Reusable environment framework across multiple projects
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SIM-002 Completed

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