From simulation to smoother robot motion
The normalized-residual policies combine higher feature rank with roughly 3× lower joint jitter in simulation, and that smoothness advantage persists on a physical Spot. We also compare representations and control smoothness across simulated quadrupeds and a humanoid.
What we measure
We evaluate deployment using two hardware measurements: the high-frequency ratio of the joint control signal (jitter) and center-of-mass linear velocity tracking RMSE. Both are recorded on the physical Spot at 50 Hz, using the same scripted sagittal velocity profile as in simulation.
In simulation, the normalized-residual family produces smoother control while matching or improving tracking. On hardware, the deep MLP shows substantial degradation in both jitter and tracking, while SimBa maintains low jitter and tracking error. These observations establish co-occurrence, not a direct causal link between effective rank and smoothness.
Hardware results
| Quantity | SimBa-XXL (8.45M) | MLP-XXL (1.89M) |
|---|---|---|
| Hardware jitter, HFR % | 0.64 [0.61, 0.66] | 3.28 [0.56, 6.00] |
| Hardware tracking RMSE, m/s | 0.066 [0.064, 0.067] | 0.176 [0.086, 0.265] |
| Tracking-error reduction | 62.7% | Baseline |
Evaluation covers 10 policy configurations, 81,313 logged steps, and 193 steady-state forward strides. Control runs at 50 Hz with 2–5 ms end-to-end latency. Intervals are 95% bootstrap confidence intervals.