Real World. Real Intent. Raw Data.

TrainRobotswith
EgocentricHuman Data

High-quality, intent-rich POV datasets to fuel the next generation of Embodied AI.

Book a Demo →Record Tasks. Get Paid →

The Problem

Roboticsisstarved

of physical reality.

Training robots for the real world requires real-world data. Capturing authentic human behavior at scale has been the missing piece.

01

Lab demos are staged

Controlled environments miss real-world messiness—clutter, varied lighting, unexpected obstacles.

02

Teleoperation doesn't scale

Manual data collection is slow and expensive. Hours of operator time for minutes of useful data.

03

Sim2Real gap persists

Simulated environments fail to capture household physics and the unpredictability of real homes.

04

Static datasets go stale

One-time data releases can't keep up. Robots need continuous, fresh data for ongoing learning.

Our Solution

Observationallearning

at scale.

Phone-first egocentric capture from real homes—focused on goal-driven, intent-rich tasks.

01

Ego-view capture

First-person perspective mirrors how robots will perceive the world—not third-party observation.

02

Household reality

Real-world messiness: clutter, varied lighting, unexpected layouts. Non-staged, diverse homes.

03

Intent over micro-motion

Long-horizon, goal-driven tasks. What to achieve matters more than exact trajectories.

04

Continuous collection

Phone-first capture scales effortlessly. Lightweight mounts, no expensive hardware.

Egocentric Capture

First-person POV

LIVE
kitchen_unload_001.mp400:47
Annotated Output

Verb + Noun labels

12 actions
0.0sopendoor
2.3spick_upglass
4.1splaceshelf
Our Approach

Macro tasks,micro annotations.

Goal-level task packs segmented into atomic actions with precise timestamps.

Goal-drivenFrame-levelMachine-ready
Raw Capture
00:47
glass
shelf
pick_up → glass
Annotated
12 actions
Our Pipeline

Fromcapture

to delivery.

Quality at every step while scaling collectionacross our global contributor network.

01

Calibrate

One-time setup test ensures proper mount and framing.

02

Capture

Contributors record household tasks from egocentric view.

03

Ingest

Secure upload via presigned URLs. Zero server contact.

04

QC

Automated + manual review for technical and task quality.

05

Annotate

Verb+Noun segmentation with precise timestamps.

06

Deliver

Versioned dataset packs for your training pipeline.

Annotation standard:Verb + Noun

Every video segmented into atomic actions following EPIC-KITCHENS vocabulary. Precise timestamps, machine-readable labels.

open+doorpick_up+glassplace+shelf
Pilot in progress

What We Collect

Pilottasklibrary,

expanding.

15-20 core household tasks focused on activitiesrobots need to master for real-world deployment.

🍳8 tasks

Kitchen

  • Dishwasher load/unload
  • Table setting/clearing
  • Food prep
  • Appliance use
👕6 tasks

Laundry

  • Clothes folding
  • Laundry sorting
  • Machine operation
  • Hanging clothes
📦5 tasks

Organization

  • Cabinet organizing
  • Shelf arrangement
  • Drawer sorting
  • Item placement
🚶4 tasks

Navigation

  • Room-to-room
  • Door opening
  • Carrying objects
  • Path planning

0

Tasks Defined

0

Categories

∞

Scalable

Explore task library

Login required

Data Schema

EgosenseHome

Taxonomy.

Built on EPIC-KITCHENS Verb-Noun schema,extended for whole-home manipulation tasks.

0+

Verbs

0+

Nouns

0+

Categories

Key Features

  • Atomic Actions (Verb + Noun)
  • Training-grade temporal grounding
  • Household object registry: tools, containers, surfaces
  • Mapping-ready for RT-X and Ego4D

Verb Categories

Generic15Kitchen20Laundry12Cleaning10Organization13
egosense_dataset.json
v1.0.1
// dishwasher_unload.json
{
"task_id": "TASK_0042",
"task": "dishwasher_unload",
"taxonomy_version": "1.0.1",
"contributor_id": "TR_0015",
"duration_ms": 47200,
"fps": 30,
"resolution": "1920x1080",
"actions": [
{
"action_id": 1,
"start_ms": 0, "end_ms": 2300,
"verb": "open", "verb_class": 12,
"noun": "dishwasher_door", "noun_class": 45
},
{
"action_id": 2,
"start_ms": 2300, "end_ms": 4100,
"verb": "pick_up", "verb_class": 5,
"noun": "glass", "noun_class": 23
},
{
"action_id": 3,
"verb": "place", "noun": "cabinet_shelf"
}
],
"metadata": {
"environment": "kitchen",
"lighting": "natural",
"device": "iPhone 14 Pro"
}
}
// laundry_folding.json
{
"task_id": "TASK_0089",
"task": "laundry_folding",
"taxonomy_version": "1.0.1",
"duration_ms": 62400,
"actions": [
{
"verb": "pick_up", "noun": "t-shirt",
"confidence": 0.94
},
{
"verb": "fold", "noun": "t-shirt",
"confidence": 0.97
},
{
"verb": "put", "noun": "drawer"
}
]
}
// table_wiping.json
{
"task": "table_wiping",
"region": "TR",
"actions": [
{ "verb": "pick_up", "noun": "cloth" },
{ "verb": "wipe", "noun": "table" },
{ "verb": "rinse", "noun": "cloth" }
]
}
// cabinet_organizing.json
{
"task": "cabinet_organizing",
"object_count": 12,
"actions": [
{ "verb": "open", "noun": "cabinet_door" },
{ "verb": "remove", "noun": "plate" },
{ "verb": "stack", "noun": "plate" },
{ "verb": "put", "noun": "shelf" }
]
}
// dishwasher_unload.json
{
"task_id": "TASK_0042",
"task": "dishwasher_unload",
"taxonomy_version": "1.0.1",
"contributor_id": "TR_0015",
"duration_ms": 47200,
"fps": 30,
"resolution": "1920x1080",
"actions": [
{
"action_id": 1,
"start_ms": 0, "end_ms": 2300,
"verb": "open", "verb_class": 12,
"noun": "dishwasher_door", "noun_class": 45
},
{
"action_id": 2,
"start_ms": 2300, "end_ms": 4100,
"verb": "pick_up", "verb_class": 5,
"noun": "glass", "noun_class": 23
},
{
"action_id": 3,
"verb": "place", "noun": "cabinet_shelf"
}
],
"metadata": {
"environment": "kitchen",
"lighting": "natural",
"device": "iPhone 14 Pro"
}
}
// laundry_folding.json
{
"task_id": "TASK_0089",
"task": "laundry_folding",
"taxonomy_version": "1.0.1",
"duration_ms": 62400,
"actions": [
{
"verb": "pick_up", "noun": "t-shirt",
"confidence": 0.94
},
{
"verb": "fold", "noun": "t-shirt",
"confidence": 0.97
},
{
"verb": "put", "noun": "drawer"
}
]
}
// table_wiping.json
{
"task": "table_wiping",
"region": "TR",
"actions": [
{ "verb": "pick_up", "noun": "cloth" },
{ "verb": "wipe", "noun": "table" },
{ "verb": "rinse", "noun": "cloth" }
]
}
// cabinet_organizing.json
{
"task": "cabinet_organizing",
"object_count": 12,
"actions": [
{ "verb": "open", "noun": "cabinet_door" },
{ "verb": "remove", "noun": "plate" },
{ "verb": "stack", "noun": "plate" },
{ "verb": "put", "noun": "shelf" }
]
}

Mapping-ready for RT-X, Ego4D, and VLA pipelines

Compatible with

🍳EPIC-KITCHENS🤖Open X-Embodiment👁️Ego4D🧠VLA Models
Delivery

What's in thepack?

Everything to integrate into your pipeline. Originals + standardized encodes included.

Original source recordings preserved in full quality. Standardized encodes available for training compatibility: 1080p, 30fps, H.264 codec. Consistent aspect ratios across all videos.

MP4 formatOriginals preservedStandardized encodes
Request sample pack
Regional Focus

Generalizationthrough

diversity.

Different home styles, clutter levels, and cultural patterns.Robust training data for models that generalize.

9
Countries
Active
Planned

Active Regions

🇹🇷
Turkey

50+

🇬🇧
UK

Q2

🇦🇪
UAE

Q2

🌐
6+ More

2025

Why Diversity Matters

01

Varied layouts

Different floor plans, room sizes, and furniture arrangements.

02

Lighting conditions

Natural and artificial light across times of day and seasons.

03

Real clutter

Non-staged homes with authentic object placement and density.

For Engineers

Technical Resources.

Integration docs, sample data, and research papers.

View all docs
Docs

API Reference

Schema docs, type definitions, and field specs

Download

Sample Data

Preview datasets with full annotations

Code

Integration

PyTorch loaders, training scripts, examples

Papers

Research

Methodology papers and benchmark results

Blog

Latest Insights.

News, updates, and thoughts on robotics and AI.

📝

Coming Soon

We're preparing great content for you.

Contact

Let's talk about your
data needs.

Research datasets or custom collection solutions—we'd love to hear from you.

hello@egosense.ai
Response within 24h

What happens next

We review your inquiry
Discovery call
Sample data access
Custom proposal
Hiring Contributors

Earn money recording everyday tasks from home

Learn More