# Why Proximity Safety for Robots Cannot Rely Solely on Vision and AI Models

**URL:** <https://discuss.px4.io/t/why-proximity-safety-for-robots-cannot-rely-solely-on-vision-and-ai-models/49401>\
**Category:** Uncategorized\
**Created:** [August 22, 2026, 6:54am UTC](https://discuss.px4.io/t/why-proximity-safety-for-robots-cannot-rely-solely-on-vision-and-ai-models/49401 "2026-08-22T06:54:39Z")\
**Posts on this page:** 1\
**Page:** 1

<div class="post-metadata">

**Author:** ![benewakelidar](https://discuss.px4.io/user_avatar/discuss.px4.io/benewakelidar/32/21414_2.png) [@benewakelidar](https://discuss.px4.io/u/benewakelidar)\
**Post date:** [August 22, 2026, 6:54am UTC](https://discuss.px4.io/t/why-proximity-safety-for-robots-cannot-rely-solely-on-vision-and-ai-models/49401/1 "2026-08-22T06:54:39Z")

</div>

At the WAIC 2026 expo, nearly every embodied AI robot demonstrating real-world tasks was enclosed within a protective barrier. Safety personnel kept their eyes glued to the same vulnerable spots: **feet, flanks, and arm trajectories.**

These three areas share a fundamental flaw— **they all lie outside the FOV of the head-mounted primary camera, occluded by the robot’s own body.**

![1](https://discuss.px4.io/images/transparent.png)

This is not a limitation of AI model capacity. Even the most advanced end-to-end model cannot infer spatial physics in areas occluded by the robot’s frame. **This is a geometry problem.** Yet, for a robot to walk autonomously among humans, it must first master this 5cm-to-1m proximity zone: _Is there a drop-off under its feet when stepping forward? Is there a human beside its body when turning around?_

![2](https://discuss.px4.io/images/transparent.png)

The industry currently offers multiple approaches, each with distinct trade-offs.

Industry leaders like Tesla and Figure place vision, multimodal AI, and end-to-end learning at the core of their control architecture, attempting to use a unified neural network to handle all sensing and execution. While this approach offers indisputable value in scene understanding and generalizability, it fails to address two critical bottlenecks: **body-occluded blind spots** , and **the massive compute overhead required for high-frequency, low-semantic spatial judgments** (_“Is an object nearby, and how far is it?”_).

![3](https://discuss.px4.io/images/transparent.png)

Thus, the architecture shifts toward a division of labor: **The AI model handles high-level understanding and task planning, while an independent ranging pipeline guards the safety baseline at close range.** Based on this deterministic input, low-level controllers handle real-time deceleration, evasion, or emergency stops.

![4](https://discuss.px4.io/images/transparent.png)

This independent safety pathway features a shorter execution loop, predictable response latency, and easily quantifiable failure modes— **making it vastly easier to benchmark, validate, and certify for mass-production acceptance.**