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Synthetic Simulation Pipelines for Embodied AI

Design high-fidelity simulation stacks that combine physics accuracy, procedural scene generation, and language-driven scenario scripting to accelerate robotics training.

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6. Sim-to-Real Transfer Strategies

Bridging the gap between simulation and reality is paramount.

  • Domain Randomization: Expose agents to wide variations to build robustness.
  • System Identification: Calibrate physics parameters by measuring real-world behavior and updating simulations accordingly.
  • Fine-Tuning: After training in simulation, conduct real-world fine-tuning with safe exploration methods.
  • Sensor Calibration: Align simulated sensors with real hardware by matching noise distributions and latency profiles.
  • Real-to-Sim Feedback: Log real-world failures and replicate them in simulation to augment training data.

Document transfer experiments meticulously—record training regimes, randomization settings, and success rates.

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