Mastering Sim2Real for Bipedal Locomotion: Domain Randomization and Latency Compensation

Bridging the Reality Gap in Bipedal Locomotion

Reinforcement learning (RL) policies trained purely in simulation often fail or vibrate destructively upon deployment to physical hardware. This guide breaks down the core techniques used by tournament-winning teams to achieve zero-shot sim-to-real transfer on high-speed bipeds like the [Tiangong Ultra](/robots/robot-model-tiangong-ultra).

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1. Primary Sources of the Sim2Real Gap

| Discrepancy Vector | Simulation Behavior | Physical Hardware Reality | | :--- | :--- | :--- | | Contact Dynamics | Rigid Coulomb friction & spring-damper approximations | Non-linear elastomeric foot deformation & track micro-slip | | Actuator Dynamics | Instantaneous torque response | Inductive delay, back-EMF, rotor friction, and temperature-dependent resistance | | System Latency | Synchronous stepping ($t = t + \Delta t$) | Asynchronous CAN-FD bus jitter (2–15 ms) and sensor pipeline delays |

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2. Systematic Domain Randomization (DR)

To prevent policies from overfitting to simulator quirks, key physical parameters must be randomized across parallel simulation environments at every episode reset:

```python # Example Isaac Lab Domain Randomization Ranges for Bipedal Locomotion randomization_config = { "payload_mass_offset": [-2.0, 3.5], # kg added to torso center of mass "com_displacement": [-0.03, 0.03], # meters along X/Y axes "ground_friction_coefficient": [0.35, 1.25],# friction variation (slick tile to rubber track) "joint_damping_multiplier": [0.8, 1.3], # joint friction and damping scale "motor_strength_multiplier": [0.85, 1.15], # torque scale to simulate voltage sag "observation_noise_imu": [0.02, 0.05], # rad/s angular velocity sensor noise } ```

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3. Action History Buffering & Latency Compensation

Rather than feeding only the instantaneous state $s_t$ into the policy MLP, the network must receive a historical sequence of past observations and actions:

$$\mathbf{O}_t = \{ s_t, a_{t-1}, a_{t-2}, a_{t-3}, a_{t-4} \}$$

This allows the neural network to implicitly infer system latency, actuator backlash, and unmodeled terrain dynamics without requiring explicit system identification models.

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4. Deployment Safety Checklist

Before powering up high-voltage joint buses on real hardware:

1. ✅ Joint Software Limit Clamps: Clamp commanded target angles strictly within mechanical endstop margins. 2. ✅ Torque Rate Limiting (Slew Rate): Enforce $\frac{d\tau}{dt} \le \tau_{\max\_rate}$ to prevent abrupt current spikes that trigger motor driver overcurrent trips. 3. ✅ Wireless E-Stop Integration: Ensure a dedicated hardware relay can drop 48V DC bus power within $< 20\,\text{ms}$ upon heartbeat loss.