Coursera for Robots
A global platform for collecting human data for robot learning
We help people record first-person experiences, anonymize them on-device, and turn them into structured datasets that can train the next generation of robots and world models.
Inspired by the future of egocentric data collection for robot learning.
The platform
What is Leleka Lens?
Leleka Lens is a platform for recording, anonymizing, labeling, and sharing first-person human data for robot learning. People use smart glasses or a simple head-mounted phone setup to capture everyday tasks from a human point of view. The platform then helps transform those recordings into structured, privacy-preserving datasets that researchers can use to train robots, world models, and embodied AI systems.
The problem
Why robot learning needs human data
Robots still struggle with the real world because real-world data is difficult and expensive to collect. Most robotics datasets are small, narrow, or captured in controlled environments. That makes it hard for robots to generalize across homes, cultures, objects, habits, and environments.
Humans, on the other hand, perform useful tasks every day: preparing food, organizing spaces, opening containers, cleaning, carrying, sorting, fixing, assembling. If this experience could be captured safely and structured correctly, it could become one of the richest training sources for robot learning.
Leleka Lens is designed to help turn everyday human experience into a living dataset for robotics.
The platform
Everything you need to collect, review, and share robot-learning data
Leleka Lens is not a single tool — it is a connected ecosystem of apps, cloud infrastructure, and developer tools designed to work together.
Leleka App
An on-device companion app that connects to your recording device — smart glasses, head-mounted phone, or action camera. It controls capture, previews anonymization in real time, and prepares clips for upload.
Leleka Cloud
A cloud platform for managing uploads, organizing datasets, running quality checks, and collaborating with research teams. The central place where raw recordings become structured, searchable data.
Leleka Player
A review and annotation tool for playing back recordings, validating anonymization results, adding or correcting labels, and inspecting metadata before data enters the shared ecosystem.
Data Collection
Workflows and task templates for structured egocentric capture — from kitchen tasks to object manipulation. Contributors follow guided flows to produce consistent, robot-learning-ready recordings.
Developer SDK
APIs and open-source libraries for researchers and developers. Access datasets programmatically, build custom processing pipelines, integrate with your own training infrastructure, and export in standard formats.
Contributor Community
A growing network of people recording everyday experiences around the world. Contributors can track their impact, earn rewards when their data is used, and help expand coverage across environments and cultures.
For contributors
A creator economy for robot data
Today, platforms reward people for entertainment, attention, and clicks. But valuable human experience in the physical world is also useful data. Leleka Lens explores a new model where contributors can benefit from helping advance robotics.
Instead of uploading videos only for social engagement, contributors can upload structured recordings that support research and real-world robot learning. As the ecosystem grows, they can be rewarded when their data is used, licensed, or downloaded.
Our goal is to make contributing useful data feel as accessible as uploading to a content platform, while keeping the technical depth required for robotics.
Who can contribute
Your everyday work is training data for robots
Imagine creating a channel — like on YouTube or Coursera — but instead of tutorials for humans, you are recording real-world workflows that help robots learn. A mechanic filming daily repairs. A cook capturing meal prep. A retail worker documenting shelf stocking. Every recording becomes structured data that robotics teams need.
Auto mechanic
#DailyCarRepairs
Films brake pad replacement, engine diagnostics, and under-hood work. Hands, tools, parts, and step-by-step procedures — all from a first-person view.
Robot learning value: Tool use, fine manipulation, multi-step assembly
Home cook
#KitchenEveryday
Records meal prep, chopping vegetables, operating appliances, and organizing a kitchen. Captures how people naturally interact with food and cooking tools.
Robot learning value: Object manipulation, kitchen navigation, sequential tasks
Furniture assembler
#Build&Fix
Documents assembling flat-pack furniture, using power tools, measuring, aligning parts, and handling hardware. Real DIY from the builder's perspective.
Robot learning value: Bimanual coordination, spatial reasoning, tool handling
Retail worker
#StoreOperations
Captures stocking shelves, folding clothes, scanning items, organizing displays, and handling inventory. Everyday retail from the worker's eyes.
Robot learning value: Object sorting, shelf interaction, repetitive pick-and-place
Lab technician
#LabProcedures
Records sample handling, pipetting, equipment calibration, and sterile workspace organization. Precise hand work in structured environments.
Robot learning value: Precision manipulation, sterile handling, instrument use
Gardener
#Garden&Grow
Films planting, pruning, watering, soil work, and tool use outdoors. Captures human interaction with natural objects and unstructured environments.
Robot learning value: Outdoor navigation, deformable objects, tool use in nature
How contributors get paid
Record your work
Wear smart glasses or mount a phone while doing what you already do every day.
Build your channel
Create a thematic collection — like a Coursera course, but made of real hands-on footage for robots.
Data gets used
Robotics labs and AI teams license your structured data for training. Your channel grows in value over time.
You get paid
Earn when your recordings are accessed, downloaded, or used in research. More data, more impact, more rewards.
The bigger picture
From simulation to human-centered world models
Today, there are two major directions in robot learning. One approach relies heavily on simulation and reinforcement learning. The other focuses on learning from human behavior and real-world demonstrations.
Leleka Lens is built around the second path. We believe robots will become more useful when they can learn from how humans actually see, move, manipulate objects, and interact with the physical world.
Our long-term vision is to help build world models trained not only on synthetic environments, but on diverse human experiences collected across the real world.
The pipeline
How Leleka Lens works
Record
Contributors record first-person video using smart glasses or a head-mounted phone rig. The goal is useful robot-learning footage: hands, objects, motion, and tasks as they naturally happen in the world.
Anonymize on device
Before anything is uploaded, Leleka Lens processes the data locally to protect privacy. Faces, screens, documents, license plates, and other sensitive signals can be blurred or removed. Contributors can preview exactly what will be shared.
Structure the data
The system breaks recordings into meaningful segments and generates metadata such as scene type, task category, object interactions, motion cues, and text summaries. This turns raw video into training-ready robotic data.
Review and label
Contributors can validate and improve labels. Researchers can also request richer annotation layers for specific tasks, objects, or action types.
Share and monetize
Approved data is uploaded into the Leleka Lens ecosystem, where labs and robotics teams can access curated subsets. Contributors are rewarded when their data creates value.
What we capture
What kind of data can be collected
Leleka Lens is designed for robot-learning-ready egocentric data, not generic social video.
Egocentric video
First-person recordings of tasks, environments, and object interactions.
Head motion and camera trajectory
Useful for understanding how a person moves through a scene.
Hand-object interaction
Important for manipulation learning and action segmentation.
Audio and spoken context
Optional multimodal signals that can help interpret the scene.
Location and environment context
Optional geolocation or place metadata when contributors allow it.
Text labels and descriptions
Human-readable summaries of what is happening in each segment.
Sample recording clips and anonymized dataset examples coming soon.
The hardware challenge
Not all first-person glasses are equal
The market for first-person recording devices is fragmented. There are consumer smart glasses, research headsets, action cameras, and simple phone-based rigs. While many of them can record video, they differ sharply in price, comfort, battery life, recording format, metadata quality, and how easy it is to extract data for research.
Some devices are affordable but closed. Some record decent video but do not expose sensor streams or developer tools. Some are designed for consumers, while others are built for research and are too expensive for large-scale public participation. This makes it hard to build a global contributor ecosystem, because even before annotation and privacy, the raw data itself is inconsistent.
Leleka Lens exists partly because this hardware layer is messy. We want to make it easier to compare devices, standardize outputs, and turn fragmented first-person recordings into a usable pipeline for robot learning.
A fragmented hardware landscape
Below is an example of how different devices vary across the dimensions that matter for robot-learning data collection.
| Device / Setup | Category | Price | Recording time | Video / Export | Sensor access | SDK support | Comfort |
|---|---|---|---|---|---|---|---|
Meta Ray-Ban Smart Glasses Most popular smart glasses, great form factor, but closed ecosystem with limited data export for research | Consumer smart glasses | Medium | Limited | Low | Limited | Limited | High |
Meta Aria / Aria-like research devices Excellent for research, expensive and limited availability | Research glasses | High | Medium | High | Strong | Strong | Medium |
Pupil Labs Powerful but too expensive for mass participation | Research headset / glasses | Very high | Medium | High | Strong | Strong | Medium |
RayNeo / Thunderbird Attractive hardware, unclear research-grade extraction | Consumer AI glasses | Medium | Limited | Low | Limited / partial | Limited / partial | High |
Rokid glasses Good accessibility, less standardized for datasets | Consumer AI glasses | Medium | Limited | Low | Limited / partial | Partial | High |
Xiaomi AI glasses Interesting hardware, unclear data openness | Consumer AI glasses | Medium | Limited | Low | Limited | Limited | High |
Phone mounted on head Cheapest path, but less stable and less wearable | DIY / mobile setup | Low | Varies | High | Medium | Strong via mobile | Low–medium |
Action camera head mount Good video, weak metadata and no native egocentric semantics | Prosumer setup | Low–medium | Medium–long | High | Low–medium | Limited | Medium |
Meta Ray-Ban Smart Glasses
Most popular smart glasses, great form factor, but closed ecosystem with limited data export for research
Meta Aria / Aria-like research devices
Excellent for research, expensive and limited availability
Pupil Labs
Powerful but too expensive for mass participation
RayNeo / Thunderbird
Attractive hardware, unclear research-grade extraction
Rokid glasses
Good accessibility, less standardized for datasets
Xiaomi AI glasses
Interesting hardware, unclear data openness
Phone mounted on head
Cheapest path, but less stable and less wearable
Action camera head mount
Good video, weak metadata and no native egocentric semantics
The exact specs vary by model, software version, and region. The key challenge is not just which device records video, but which device can produce consistent, privacy-safe, robot-learning-ready data.
Trust & safety
Privacy is not a feature. It is the foundation.
Human-centered data collection only works if contributors trust the system. That means privacy cannot be an afterthought or hidden in policy language.
Leleka Lens is built around on-device anonymization, transparent preview, and contributor control. People should be able to see exactly what information is being removed, what is being uploaded, and what rights they keep.
We want contributors to feel that they are participating in a fair and understandable data ecosystem — not giving away invisible value.
Preview before upload
See exactly what will be shared before any data leaves your device.
Control what you share
Choose which parts of your recordings are included and which are removed.
Trust through transparency
Open processes and clear policies so you always understand the system.
For researchers
Built for researchers, robotics labs, and embodied AI teams
Real-world robot learning depends on diverse, well-structured human data. Labs need more than raw video. They need searchable, annotated, privacy-safe datasets that can be filtered by task, objects, environments, motion, and quality.
Leleka Lens aims to provide a living dataset, continuously expanded by contributors across locations, environments, and behaviors. This could help robotics researchers access broader, more realistic human experience than a closed lab collection process alone.
Example dataset filters
The landscape
Datasets the community is already building
The robotics community is actively investing in real-world human data. Research groups and companies are releasing increasingly ambitious datasets that demonstrate why this direction matters — and how much more is needed.
Leleka Lens is designed to complement and accelerate this ecosystem by making it easier for anyone to contribute structured, privacy-safe egocentric data at global scale.
These are examples of the growing investment in real-world robot learning data. As more datasets emerge, the need for standardized collection, privacy infrastructure, and contributor tooling becomes even more important.
Our approach
More than a dataset. A living ecosystem.
Most datasets are static snapshots. Leleka Lens is imagined as a living ecosystem that keeps growing with new contributors, new environments, new objects, and new task categories.
It combines capture, anonymization, labeling, curation, and monetization into one workflow. This makes it possible to think beyond one benchmark and toward a long-term infrastructure layer for robot learning.
In the future, this could support richer human-to-robot transfer, better generalization, and more grounded world models trained on real human experience.
Personal note
Why I'm building this
“I am building Leleka Lens because I believe the future of robotics will depend on learning from real human experience — not only from simulation. Every day, people interact with objects, tools, homes, and environments in ways that are rich with information for robots. But today this experience is mostly lost.
I want to help create a platform that makes it possible to collect this data responsibly, protect privacy, and turn it into something that can help robots better understand the world.
As someone from Ukraine, I also wanted to build this project with a name and identity that reflects resilience, movement, and observation. That is why I chose the name Leleka Lens.”
See it in action
See the idea in action
Examples will show recording setup, privacy filtering, and how raw footage becomes robot-learning data.
2-minute project video
Overview of Leleka Lens and the vision behind it
Sample recording — first-person egocentric capture
Example of raw footage before anonymization and structuring
Help build the future of robot learning
Leleka Lens is an early vision for a global human-data platform for robotics. If you are a researcher, builder, contributor, or potential collaborator interested in privacy-preserving egocentric data for embodied AI, I would love to connect.