Train in simulation. Fine-tune in the real world.
Physics-informed simulation pre-training with online RL fine-tuning. Transfer policies from Isaac Lab to real industrial environments with minimal sim-to-real gap — then continuously improve from operational data.
Training pipeline
How it works
Sim-to-real with minimal gap.
Physics Simulation Pre-Training
Isaac Lab environments model the target domain — warehouse floors, mine sites, power substations, factory floors — with physics-accurate dynamics, sensor models, and domain randomization.
Distributed Training
Ray Train DDP with MLflow autologging. Hyperparameter optimization via Ray Tune + ASHA + Optuna TPE. Train at scale, track every experiment.
Online RL Fine-Tuning
Deploy to the real environment and continue learning from operational data. EWC regularization prevents catastrophic forgetting of simulation-learned behaviors.
Closed-Loop Evaluation
Isaac Lab promotion gates validate every model update before deployment. Oracle-labeled online metrics confirm real-world performance matches simulation predictions.
Policies that improve with every shift.
Pre-train in physics simulation, fine-tune in the real world, and continuously improve from operational data — safely.