---
name: tai-ch158-embodied-robotics-sensor-fusion-perception-and-world-m
description: 'Apply chapter 158 of Testing AI, Embodied Robotics: Sensor Fusion, Perception, and World Models, 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: sensor fusion, perception, and world models.'
---

# Embodied Robotics: Sensor Fusion, Perception, and World Models

Skill name: `tai-ch158-embodied-robotics-sensor-fusion-perception-and-world-m`

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

## Purpose

Robots fail when the world they think they see is not the world they are actually in.

## 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

A robot acts on a model of the world. That model comes from cameras, microphones, lidar, radar,
depth sensors, tactile sensors, encoders, maps, memory, and sometimes language. If perception is
wrong, planning and action can look irrational even when the planner is doing exactly what it
was told.

## 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, separate perception metrics from task metrics. Use calibrated confidence,
disagreement detection, sensor ablation, robustness tests, adversarial physical examples, and
replayable sensor logs. The most useful bug report often starts with "the robot believed the
world looked like this."