---
name: tai-ch156-embodied-robotics-power-latency-and-operating-cost
description: 'Apply chapter 156 of Testing AI, Embodied Robotics: Power, Latency, and Operating Cost, as a workflow for evaluating AI and non-deterministic systems. Use for test planning, eval design, quality review, release evidence, examples, or coaching related to embodied robotics: power, latency, and operating cost.'
---

# Embodied Robotics: Power, Latency, and Operating Cost

Skill name: `tai-ch156-embodied-robotics-power-latency-and-operating-cost`

Based on **Testing AI: Engineering Confidence in AI Systems** by **Jason Arbon**.

## Purpose

Robots are constrained by batteries, heat, time, compute, parts, maintenance, and the cost of
every physical mistake.

## Use This Workflow

- Identify the AI behavior or release decision being evaluated.
- Define realistic cases, slices, unacceptable outcomes, and evidence needed for confidence.
- Choose measurements that match the risk: rubric scores, samples, intervals, traces, human review, deterministic checks, or production monitors.
- Report uncertainty, severe failures, and decision impact instead of only a pass/fail result.

## Key Guidance

Embodied AI quality includes economics. A robot that technically works but drains its battery,
overheats, moves too slowly, burns cloud tokens, damages parts, needs constant human rescue, or
blocks a workflow is not production-ready.

## Apply The Approach

Create representative cases, score them with explicit criteria, review severe failures separately, report uncertainty, and connect the evidence to a concrete decision.

## Expert Notes

At expert level, measure p50, p95, and p99 latency; energy by subsystem; model-route decisions;
local versus cloud inference; failure cost; and marginal quality gain per additional dollar. The
best architecture is often a tiered system, not a single giant model doing everything.