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S2R Sim-to-Real Transfer Degrades on Stiff, Overdamped Gains

See how gain settings affect the sim-to-real gap in reaching tasks.

Paradox. Stiff, overdamped gains yield the best system identification but the worst sim-to-real transfer.
Failure mode. High-frequency oscillation from aggressive controller + neural network policy interaction.
Solution. Compliant, underdamped gains act as a natural low-pass filter for reliable transfer.

We study reaching tasks with a Franka Research 3 robot to directly isolate the motor-level sim-to-real gap. For each gain setting, we perform gain-specific system identification, train RL policies in the calibrated simulation, and deploy them on the real robot without fine-tuning.

Stiff, overdamped gains are easiest to model. The MSE after system identification is over an order of magnitude lower for stiff, overdamped gains. Tap any cell to see how the identified simulator tracks the real robot.

System Identification Error

Sysid Error (log MSE). Darker blue = lower error. Tap a cell to view response curves.

Epoch -

Response Curve. Blue = real, red = simulated, gray dashed = desired position. Scrub epochs to watch the fit improve.

…but hardest to transfer. Paradoxically, stiff and overdamped gains produce the worst sim-to-real transfer. The dominant failure mode is high-frequency oscillation that amplifies small prediction errors into out-of-distribution states.

Sim-to-Real Transfer Error

Sim2Real Error (MSE). Darker blue = lower error (better transfer). Tap a cell to compare trajectories.

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Trajectory Comparison. Blue = real robot, red = simulated. Top = position, bottom = velocity. Shaded area = gap.

Zero-shot sim-to-real transfer. With compliant gains (left), the arm smoothly approaches the target. With stiff, overdamped gains (right), the same policy architecture produces violent high-frequency jittering — tap the sound icon to hear it.

Compliant × Underdamped

tap to hear jitter

Stiff × Overdamped

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