Internet Data Limited
Internet data alone cannot capture the physical interactions robots need.
A source for quality ethical data collection We prioritize privacy amongst all else
From Vendor Activity to AI-Ready Data
01. Gather Data From Vendors
We capture real-world human activity across our vendor network, covering diverse tasks, environments, and workflows.
02. QA & Validate Footage
Every recording is reviewed for quality, relevance, and consistency. Unusable footage is removed before it enters the dataset.
03. Transform Into AI-Ready Data
Validated footage is structured with metadata, labels, and sensor information, creating datasets ready for physical AI training.
The industry is converging on the same problem
Across robotics companies and research labs, the message is consistent: physical AI needs more diverse, real-world data, and collecting it at scale remains difficult.
Human Data Needed
Human demonstrations provide valuable actions and context for training.
Data Hard to Collect
Real-world data is costly and difficult to gather consistently at scale.
Diverse Settings Needed
Robots need data across different environments, tasks, and conditions.
Explore the evidence
Research, company statements, datasets, and industry commentary supporting the Athla Labs thesis.
The data needed to scale a truly general purpose robot doesn't exist on the internet - it has to come from the real world
Unlike vision or language, robotics lacks a large-scale equivalent—no "YouTube for robot behaviors."
Unlike LLMs, we can’t scrape the internet for robot data.
It must be collected, one interaction at a time, in the real world.
You can’t just search the web for millions of examples.
Teaching autonomous robots and vehicles how to interact with the physical world requires vast amounts of high-quality data.
a small team of human demonstrators can create the same amount of training data it would otherwise take thousands of people to produce.
This includes video, ego-centric human experience, interaction data, simulation, robot data collected with remote operation, and on-policy NEO data.
Adding egocentric human data improves generation quality on both New Tasks and T2I splits.
created a dataset API for NVIDIA to access data collected from 1X offices and employee homes
Memory Developers (MDs) are data collectors who use our Skill Capture Gloves to train our models on a wide diversity of tasks.
Some AI labs are already paying XDOF to do it.
The company employs around 1,000 contractors nationwide who wear data-collection equipment at home or in factories
the supply of data necessary to train AI models used to run humanoid robots, also known as embodied AI platforms, is far smaller.
Teleoperation can be difficult to scale and require significant human effort.
includes nearly 1,300 hours of video capture (including 221 egocentric hours) across 5,035 videos.
However, collecting such a dataset is too resource-intensive for any single lab.
collecting robot manipulation data in diverse environments poses logistical and safety challenges and requires substantial investments in hardware and human labour.
adding 1 hour of additional hand data is significantly more valuable than 1 hour of additional robot data.
portable, low-cost, and information-rich data collection for challenging bimanual and dynamic manipulation demonstrations.
Current approaches rely heavily on teleoperated demonstrations which are difficult to scale.
With only 30 minutes of human videos per task, HumanEgo achieves 92.5% average success across four real-world tasks
Humans interact constantly with the physical world, yet this rich data resource remains largely untapped in robot learning.
lack of a data pipeline for humanoids to learn autonomous skills from egocentric vision.
This paper investigates a more scalable data source, egocentric human demonstrations, to serve as cross-embodiment training data for robot learning.
over 1 million trajectories across 217 tasks in five deployment scenarios
In Robotics, scaling is hindered by the lack of access to massive robot datasets.
Robotics companies are realizing that scaling data collection for general-purpose AI models is really hard. What does scale? Human data (believe it or not!)
Introducing EgoVerse: an ecosystem for robot learning from egocentric human data.
Since the robotics community can’t rely on scraping the Internet for training data, we need to create that data ourselves.