$ cat ~/projects/policy-simulation-integration.md
ML Policy + Simulation Integration
internal#Simulation#Robotics#Policy Evaluation#PyTorch#Automation
Integrating Vision-Language-Action models with robotics simulation environments for automated evaluation, training data generation, and policy validation.
Built the integration layer between trained VLA policies and robotics simulation environments. This system enables automated evaluation of manipulation policies across hundreds of task variations, collects training data from simulated rollouts, and validates policy behavior before deployment to real hardware.
// key_highlights
- ▸Simulation environment integration with standard robotics frameworks
- ▸Automated evaluation loop running hundreds of policy rollouts
- ▸Metrics collection and analysis for success rate, trajectory quality, and generalization
- ▸Architecture for seamless switching between simulation and real hardware inference
- ▸Data collection pipeline for generating synthetic training demonstrations
This is proprietary work from my role at Agile Robots SE. Source code is not publicly available, but the write-up above describes the architecture and technical approach.