> ## Documentation Index
> Fetch the complete documentation index at: https://docs.vangrid.io/llms.txt
> Use this file to discover all available pages before exploring further.

# Embodied AI: Continuous Spatial Ground Truth for Robots

> Vangrid provides the continuous spatial ground truth that embodied AI and robotics systems need to perceive, navigate, and act in the physical world.

Embodied AI systems — humanoid robots, drone swarms, autonomous industrial equipment — require a persistent, continuously updated model of the physical world to operate safely and effectively. Vangrid supplies the spatial cortex: a real-time stream of verified ground truth observations from 3B+ edge nodes that your system can ingest to keep its world model current.

## What embodied AI needs from spatial data

A world model is only as good as its inputs. Vangrid addresses three core requirements:

* **Continuous updates** — the physical world changes constantly; your world model must too
* **Verified observations** — every data point carries a `provenance_hash` so your system can trust what it receives
* **High spatial density** — tactical urban coverage means enough node overlap to corroborate observations and eliminate false positives

## Streaming spatial updates into your world model

Use the Vangrid Streaming API to subscribe to a geographic zone and receive push updates as ground truth changes.

<Steps>
  <Step title="Define your area of interest">
    Specify the geographic polygon your system operates within. Keep the AOI tight to reduce update volume and latency.
  </Step>

  <Step title="Open a streaming connection">
    Connect to the streaming endpoint with your API key. Updates arrive as newline-delimited JSON.
  </Step>

  <Step title="Ingest updates into your world model">
    Parse each event and update your spatial representation. Use `ground_truth_score` to weight observations.
  </Step>

  <Step title="Verify provenance on critical observations">
    For safety-critical decisions, verify the `provenance_hash` before acting on an observation.
  </Step>
</Steps>

```python Python theme={null}
import requests
import json

API_KEY = "your-api-key"
BASE_URL = "https://api.vangrid.io/v1"

aoi = {
  "type": "Polygon",
  "coordinates": [[
    [-122.4194, 37.7749],
    [-122.4094, 37.7749],
    [-122.4094, 37.7849],
    [-122.4194, 37.7849],
    [-122.4194, 37.7749]
  ]]
}

with requests.post(
    f"{BASE_URL}/spatial/stream",
    headers={"Authorization": f"Bearer {API_KEY}"},
    json={"aoi": aoi, "min_ground_truth_score": 0.80},
    stream=True
) as response:
    for line in response.iter_lines():
        if line:
            event = json.loads(line)
            # Ingest into world model
            update_world_model(event)
```

## Key data fields for embodied AI

Each streaming event includes:

| Field                | Type        | Description                                           |
| -------------------- | ----------- | ----------------------------------------------------- |
| `geometry`           | GeoJSON     | Location and shape of the observed feature            |
| `ground_truth_score` | float (0–1) | Multi-node corroboration confidence                   |
| `timestamp`          | ISO 8601    | When the observation was captured                     |
| `provenance_hash`    | string      | Cryptographic proof of data origin                    |
| `node_count`         | integer     | Number of edge nodes that contributed                 |
| `observation_type`   | string      | Category of observation (obstacle, occupancy, change) |

<Tip>
  For real-time navigation, filter to `ground_truth_score >= 0.75`. For safety-critical actions (grasping, human interaction), require `ground_truth_score >= 0.90`.
</Tip>

## Example streaming event

```json theme={null}
{
  "event_id": "evt_8f3a2c1d",
  "timestamp": "2026-05-22T10:14:33.412Z",
  "geometry": {
    "type": "Point",
    "coordinates": [-122.4150, 37.7800]
  },
  "observation_type": "obstacle",
  "ground_truth_score": 0.94,
  "node_count": 12,
  "provenance_hash": "sha256:a3f8e2c1b4d7f9e0a2b5c8d1e4f7a0b3c6d9e2f5"
}
```

<Note>
  Contact [hello@vangrid.io](mailto:hello@vangrid.io) to discuss high-frequency streaming rates and dedicated node allocation for production embodied AI deployments.
</Note>