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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.

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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

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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.
1

Define your area of interest

Specify the geographic polygon your system operates within. Keep the AOI tight to reduce update volume and latency.
2

Open a streaming connection

Connect to the streaming endpoint with your API key. Updates arrive as newline-delimited JSON.
3

Ingest updates into your world model

Parse each event and update your spatial representation. Use ground_truth_score to weight observations.
4

Verify provenance on critical observations

For safety-critical decisions, verify the provenance_hash before acting on an observation.
Python
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)

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Key data fields for embodied AI

Each streaming event includes:
FieldTypeDescription
geometryGeoJSONLocation and shape of the observed feature
ground_truth_scorefloat (0–1)Multi-node corroboration confidence
timestampISO 8601When the observation was captured
provenance_hashstringCryptographic proof of data origin
node_countintegerNumber of edge nodes that contributed
observation_typestringCategory of observation (obstacle, occupancy, change)
For real-time navigation, filter to ground_truth_score >= 0.75. For safety-critical actions (grasping, human interaction), require ground_truth_score >= 0.90.

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Example streaming event

{
  "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"
}
Contact hello@vangrid.io to discuss high-frequency streaming rates and dedicated node allocation for production embodied AI deployments.