Section 45.5: Energy efficiency; sim-to-real and safety in locomotion

"A locomotion policy that ignores heat and power is outsourcing the hard part to the battery."

A Field Deployment Checklist

A standard locomotion energy measure is cost of transport, $\mathrm{CoT} = E / (mgd)$, where $E$ is energy spent over distance $d$ for mass $m$. It lets researchers compare controllers across robot size and runtime. The same controller can improve speed while worsening CoT enough to make battery-limited missions impossible.

Sim-to-real transfer is best framed as a residual model problem. Let $x^{\mathrm{real}}_{t+1} = f_{\mathrm{sim}}(x_t, u_t) + \delta(x_t, u_t)$. The purpose of system identification, randomization, and fine-tuning is to shrink the residual or make the policy robust to it. Safety logic must then operate on the residual-aware closed loop, not on the simulator fantasy.

Theory

A field-ready locomotion controller solves a multi-objective problem. You are trading task time, energy, actuator temperature, contact stress, and safety margins simultaneously. Optimizing one while hiding the others is how impressive lab videos become expensive hardware failures.

Transfer methods are only comparable when evaluated on the same hardware panel. Domain randomization, actuator modeling, residual learning, and hardware fine-tuning all help, but their value changes with how much of the real residual is actually represented.

Safety logic should be layered. Fast inner loops prevent immediate falls or torque spikes. Slower supervisory logic can reduce speed, widen gait, trigger re-localization, or stop the robot when the residual leaves the validated envelope.

Worked Example

A small deployment summary can expose whether a faster policy is actually the better field controller once energy and safety are priced in.

energy_j = 18200
mass_kg = 48
distance_m = 320
g = 9.81
safety_stops = 2

cot = energy_j / (mass_kg * g * distance_m)
print(f"cost_of_transport={cot:.3f}")
print({"safety_stops": safety_stops, "deployment_ok": safety_stops <= 1 and cot < 0.14})

Expected output interpretation. The energy figure is acceptable, but the safety-stop count fails the deployment criterion. This is exactly why energy and safety must live in the same artifact rather than in separate dashboards.

Practical Recipe

1. Define deployment thresholds for CoT, thermal excursions, safety stops, and velocity tracking before testing.
2. Replay the same command traces in simulation and on hardware to estimate the residual gap.
3. Evaluate the same scenario panel under nominal and degraded conditions, including battery sag or low-friction patches.
4. Add runtime monitors that can reduce speed or halt before the low-level controller saturates.
5. Store every deployment run as a transferable artifact with telemetry, summary metrics, and intervention labels.

This section is where embodied AI becomes operations engineering. Students often discover here that the hardest variables are not abstract control gains but current draw, actuator wear, battery chemistry, and the organizational cost of false safety stops.

It also clarifies why sim-to-real should never be treated as one scalar gap. The transfer residual has structure: delays, friction mismatch, compliance, sensor timing, estimator drift, and operator response. Good books teach readers how to decompose that structure.

When field transfer fails, assign blame to the dominant residual first: actuator model, terrain mismatch, sensing delay, estimator drift, or safety supervisor interaction. Otherwise teams waste time tuning the policy around the wrong bottleneck.