The Sim2Real Closed-Loop Iteration

Learning objectives: master the hardware testing procedure, methods for diagnosing jitter/drift/falling, and implement the closed-loop iteration of "simulation training → hardware testing → data collection → model correction → retraining." This is the ultimate engineering capability of Sim2Real.

15.1 Hardware Testing Tiers

15.2 Diagnosing Jitter, Drift, and Falling on Hardware

Symptom A: Sustained jitter

Troubleshooting order:

1. BAM parameters: inaccurate bandwidth/backlash → re-calibrate (BAM Actuator Modeling and Identification);
2. Action smoothing: reward function missing the smoothing penalty → retrain;
3. Control-loop timeout: inference too slow, unstable control period (see Policy Deployment and On-Device Inference).

Symptom B: Attitude drift (slowly tilting)

Troubleshooting order:

1. IMU bias: not six-face calibrated → Servo and IMU Calibration;
2. IMU mounting tilt: not compensated → add compensation in firmware;
3. Center-of-mass offset: asymmetric assembly → re-weigh per 3D Printing, Assembly, and Tolerances.

Symptom C: Frequent falling

Troubleshooting order:

1. Observation/action scaling: dimension or scaling misaligned → check_obs_act_space.py;
2. MJCF inertia error: wrong center of mass → Automated Onshape→MJCF Modeling;
3. Whole-machine mass differs from simulation: different infill → cover via domain randomization or re-calibrate.

15.3 Hardware Data Collection (the Key to the Closed Loop)

Record IMU, joint angles, policy output, and timestamps on hardware, saving to data/real_robot_logs/:

t,imu_qw,imu_qx,imu_qy,imu_qz,q0,...,q13,a0,...,a13

This data is the basis for system identification and parameter correction, and the source of the book's appendix experiment data.

15.4 Reverse System Identification (Advanced Closed Loop)

Use hardware excitation data to inversely identify the whole-machine inertia and correct the bias in the CAD-exported model:

1. Apply excitation to the robot (small oscillation, step);
2. Record the IMU + joint response;
3. Inversely fit the true mass/center of mass/inertia via least squares;
4. Write the identification result back to MJCF and retrain.

# script: scripts/calibration/system_id_fit.py
# input: hardware excitation CSV + current MJCF
# output: recommended corrected inertia parameters

This is an advanced academic Sim2Real technique that corrects residual errors (printing, assembly) left by Onshape geometric computation.

15.5 The Closed-Loop Iteration Process

simulation training → ONNX export → hardware test
                        ↓ (collect data)
                diagnose → correct BAM/MJCF/domain randomization
                        ↓
                  retrain → re-test (iterate)

Record each iteration cycle in experiments/, forming a reproducible iteration history.

15.6 Gait Performance Metrics

To quantify progress, define metrics:

• walking speed (m/s);
• standing-stability duration;
• disturbance robustness (can it recover from a push);
• fall-recovery success rate;
• turning-angle accuracy.

These metrics are compared against the original Pollen MicroDuck, forming the book's performance-evaluation material (extended in Task Extension).

15.7 Engineering Pitfall Log

• Changing everything in one iteration: BAM, MJCF, and reward were all changed at once, so the problem still couldn't be located. Change only one variable at a time and record each result.
• Hardware data without timestamps: IMU and joint timelines can't be aligned. Synchronize clocks before collection.

15.8 Chapter Summary

You have mastered hardware gait diagnosis and the closed-loop iteration, and can continuously bring the simulation model closer to the real robot.

The next chapter extends tasks, turning MicroDuck from "able to walk" into "able to walk in many ways."