AI Model Training (Sim-to-Real)

Phase 1 constructs the training environments that produce transferable policies. We build physics-accurate simulation stages in Isaac Sim, replicating the target deployment environment's geometry, material properties, and lighting conditions with measured parameters — surface friction coefficients from tribometer readings, object mass and inertia from CAD data, deformable-body properties from material testing. Domain randomization is designed as a first-class architectural concern: physics parameters (friction, restitution, joint damping) vary within measured uncertainty bounds, visual parameters (textures, lighting, object colors) span deployment-plausible ranges, and geometric parameters (object dimensions, obstacle positions) cover the expected operational variability. We configure multi-fidelity environment tiers: a lightweight tier with simplified geometry for rapid policy exploration at thousands of environments per GPU, and a high-fidelity tier with detailed meshes and ray-traced rendering for final validation. Sensor models — cameras, depth sensors, force-torque, joint encoders — are calibrated against physical hardware measurements with characterized noise profiles. Deliverables include the simulation environment package, domain-randomization configuration with parameter justification, multi-fidelity tier definitions, sensor-calibration reports, and environment benchmarks documenting simulation throughput across hardware configurations. These environments feed Phase 2's distributed training infrastructure.

Phase 2 builds the scalable training infrastructure. We architect distributed GPU training pipelines using Isaac Lab's vectorized environment framework, scaling to thousands of parallel environment instances across multi-node DGX clusters. Experiment tracking (Weights & Biases or MLflow) captures every training run's hyperparameters, reward curves, evaluation metrics, and model checkpoints, enabling reproducible comparison across algorithm variants. Hyperparameter optimization uses Bayesian search over learning rates, discount factors, network architectures, and domain-randomization schedules, with early stopping to terminate underperforming configurations. We implement modular training scripts that support algorithm swapping — PPO, SAC, DDPG for reinforcement learning; BC, DAgger, GAIL for imitation learning — with a common evaluation protocol ensuring fair comparison. Data pipelines handle demonstration datasets for imitation learning: expert trajectories collected via teleoperation or scripted planners are stored in standardized formats with state-action-reward tuples and visual observations. Training infrastructure includes automated checkpoint management with best-model selection based on evaluation-suite performance, and model-registry integration for promoting trained policies to downstream deployment pipelines. Deliverables include training pipeline code, experiment-tracking configuration, hyperparameter search configurations, model-registry setup, and infrastructure sizing guidelines for training-cluster procurement.

Phase 3 bridges the domain gap between simulation and physical reality. We deploy trained policies onto physical hardware and execute structured transfer experiments that quantify performance degradation across key metrics — task success rate, trajectory accuracy, cycle time, and safety-margin compliance. System identification refines simulation parameters using real-world measurements: we run calibration trajectories on the physical system, record joint-level dynamics and sensor outputs, and optimize simulation parameters (friction, damping, actuator response curves, sensor latency) to minimize the prediction error between simulated and physical rollouts. Domain-adaptation techniques — adversarial training, feature alignment, progressive neural networks — are applied when system identification alone cannot close the gap. We implement automated calibration loops: deploy policy → measure real-world performance → update simulation parameters → retrain → redeploy, converging toward zero-gap transfer. Reality-gap measurement dashboards track transfer metrics across calibration iterations, providing engineering visibility into which simulation parameters most influence real-world performance. Calibration data is versioned alongside simulation environments, enabling regression detection if hardware changes alter system dynamics. Deliverables include calibration procedure documentation, system-identification scripts, transfer-metric dashboards, adapted simulation environments, and a gap-analysis report with recommendations for further closure.

The final phase operationalizes trained models for production use. Policies are packaged as inference-optimized runtimes — TensorRT engines for GPU platforms, ONNX models for cross-platform compatibility — with documented input/output specifications, latency profiles, and accuracy benchmarks. Deployment configurations specify hardware requirements, driver versions, and runtime dependencies for each target platform: Jetson AGX Orin for edge robotics, T4 instances for cloud inference, and workstation GPUs for development iteration. A/B testing infrastructure enables controlled rollout: new policy versions serve a fraction of production traffic while baseline models handle the remainder, with automated statistical comparison of task-completion rates, cycle times, and safety events. Model monitoring tracks inference latency, prediction confidence distributions, and out-of-distribution input detection, triggering alerts when operational conditions drift beyond the training distribution. We establish a continuous-improvement pipeline: production telemetry feeds back into simulation environments, expanding scenario libraries with real-world edge cases, and triggering automated retraining workflows. Rollback procedures ensure that any policy regression can be reverted within minutes. Deliverables include deployment packages, A/B testing configuration, monitoring dashboards, rollback procedures, continuous-improvement pipeline documentation, and an operational playbook for model lifecycle management.