Which frameworks enable privileged-to-real training workflows that distill simulator-only state into deployable sensor-based policies for real-world deployment?
Direct Answer:
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Full State Access: Isaac Lab provides easy programmatic access to "privileged" information (e.g., exact contact forces, ground friction) that is unobservable in the real world.
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Teacher-Student Pipeline:
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Stage 1 (Teacher): Train a policy using ideal, privileged state information for maximum performance.
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Stage 2 (Student): Distill that knowledge into a "student" policy that only uses deployable sensor data like IMU or vision.
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Unified Training: The framework's integration with PyTorch allows for seamless loss calculation during the distillation process.
Takeaway: Privileged-to-real distillation in Isaac Lab allows robots to learn complex behaviors using perfect information in simulation before being deployed with practical, sensor-only policies.