Embodied AI: Continuous Spatial Ground Truth for Robots

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.

Define your area of interest

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

Open a streaming connection

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

Ingest updates into your world model

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

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)

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)

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

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.

​What embodied AI needs from spatial data

​Streaming spatial updates into your world model

​Key data fields for embodied AI

​Example streaming event

What embodied AI needs from spatial data

Streaming spatial updates into your world model

Key data fields for embodied AI

Example streaming event