Humanoid robots are being built right now — by the world's most ambitious companies.
But a robot without knowledge of the physical world is just expensive hardware.
We are building the knowledge.
India · Bangalore · 2026
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HUMAN PHYSICAL INTELLIGENCE·STRUCTURED FOR ROBOTS·BUILT FROM INDIA·FOR THE WORLD·REAL ENVIRONMENTS·REAL WORKERS·REAL DATA·HUMAN PHYSICAL INTELLIGENCE·STRUCTURED FOR ROBOTS·BUILT FROM INDIA·FOR THE WORLD·REAL ENVIRONMENTS·REAL WORKERS·REAL DATA·
The Problem
The hardware is extraordinary.
The data is missing.
Billions of dollars are flowing into humanoid robots. The actuators, the balance systems, the dexterity — all of it is advancing fast. But a robot can only do what it has been shown.
Today, there is no large-scale, structured dataset of how humans actually perform physical work. No one is capturing the way a cleaner scrubs a countertop, or how a technician threads a wire — with the precision robots need to learn from it.
🤖
Hardware is racing ahead
Companies like Figure, Tesla, 1X, and Agility are building robots that can walk, grip, and balance. The mechanical problem is being solved — but without training data, these machines sit idle.
📡
Simulation isn't enough
Synthetic environments can't replicate the unpredictability of a real kitchen, hospital room, or warehouse floor. Robots need data from real human hands in real environments — not rendered approximations.
🧠
Nobody is doing the showing
Language models had the internet. Vision models had ImageNet. Robot AI has no equivalent. There is no structured, multi-modal dataset of human physical intelligence — until now.
$0Invested in humanoid robotics globally
~0Large-scale physical task datasets
0Robot companies needing training data
0Synchronized data per capture session
The robots are ready to learn.
Someone needs to teach them.
What we do.
We partner with skilled workers — cleaners, cooks, technicians, repair specialists — and capture exactly how they do their jobs. Then we structure that knowledge into training data that robot AI can learn from.
01
Partner with skilled workers
We work with service platforms like Urban Company and Snabbit — networks of trained professionals already performing physical tasks in real homes, hotels, hospitals, and factories every day. These are the people who know how to work with their hands.
02
Capture every dimension of the task
Workers will wear our custom sensor system — cameras, motion trackers, instrumented gloves — designed to capture five synchronized data streams simultaneously: vision, hand position, full body motion, spatial environment, and force. Not video. Structured, multi-modal data.
03
Structure it for robot learning
Raw sensor data will be processed into action primitives — the exact format robot AI companies use for training. Every grasp, every motion, every force decision — labeled, timestamped, and formatted so a robot policy can learn directly from it.
We collect from everyone.
We build for
the builders.
Robot Builders
Tesla Optimus
Figure AI
1X Technologies
Agility Robotics
Google DeepMind
Boston Dynamics
Humanoid labs globally
Data Sources
Urban Company
Snabbit
Hotel chains
Hospitals
Warehouses
Cloud kitchens
Elder care centers
Research Partners
NVIDIA Research
Microsoft Research
IIT Robotics Labs
IISc
AI research institutes
University labs globally
Why This Matters
The gap nobody is talking about.
01
Simulation doesn't transfer to reality
Robots trained entirely in simulation fail in the real world. Homes are messy. Objects are unpredictable. Human environments are infinitely varied. There is no substitute for data collected in real spaces by real people.
02
Video alone is not enough
Watching a human fold laundry doesn't teach a robot how hard to grip, where to apply force, or how to recover when a garment slips. Robots need multi-modal data — vision, motion, force — not just camera footage.
03
India is uniquely positioned to solve this
A trained professional workforce. Linguistic and environmental diversity no other country can match. Real-world variety at a scale that makes our dataset genuinely different from anything collected in a lab or a standardized environment.
Our Mission
India's skilled workers are teaching the world's robots how to work.
This is not a data labeling company. This is not a gig workforce platform. This is not a video dataset provider.
Dexel Labs is building something that has never been built before — a comprehensive, structured library of how humans perform physical work, captured at scale across industries, environments, languages, and cultures.
Every session we capture will make robots slightly more capable of understanding the physical world. Every worker who participates will be contributing their expertise — their knowledge of how to grip a surface, navigate a cluttered kitchen, or fold a shirt correctly — to something that will outlast that single task.
We are at the beginning of this. We are building it carefully and honestly. And we are building it from India.
NOW
Be Part of It
This is your invitation.
Whether you want to build with us, partner with us, or follow the work — we want to hear from you.
A robot cannot learn from incomplete data. So we're building a capture system designed to miss nothing — from the exact angle of a wrist to the force on a fingertip.
The Wearable System
What the worker wears.
01 — Vision
Three Synchronized Cameras
One camera on the chest captures the full workspace. Two cameras on both wrists capture close-up hand-object interaction. All three are hardware-synchronized to under one millisecond. We see exactly what the worker sees — and what their hands are doing.
1080p · 30fps150° wide FOVHardware sync
02 — Hands
22 DoF Instrumented Gloves
Custom-designed gloves with flex sensors and IMUs across all five fingers — both hands — targeting 100Hz. Engineered to capture the exact position of every finger joint, grip type, and contact force at the fingertips. This is the data almost every other dataset misses entirely.
22 DoF per hand100HzFingertip force
03 — Body Motion
17-Point IMU Suit
Sensor pods worn on 17 body landmarks capture full-body kinematics — spine, shoulders, elbows, wrists, hips, knees — at 60Hz. Every posture shift, every weight transfer, every approach to a task captured in three-dimensional space.
17 IMU points60HzGlobal + local coords
04 — Environment
Depth Camera + Fixed RGB
An Intel RealSense depth camera maps the 3D geometry of the workspace — where objects are, how they move, what changed during the task. Fixed overhead and side cameras give the complete spatial picture the wrist cameras alone cannot provide.
RealSense D435Point cloud15fps
05 — Sync Hub
Central Recording Unit
The most underrated component. A custom-built recording unit worn on the belt broadcasts a single hardware clock pulse to every sensor simultaneously. Every data point across all five streams shares one timestamp. Drift under one millisecond — session-long.
<1ms driftHardware clockLocal SSD + cloud
06 — Structuring
Action Primitive Labeling
Raw sensor data alone is not training data. Our annotation pipeline will break every session into labeled action primitives — reach, grasp type, apply force, move, release — aligned to the state and action spaces that robot policies actually train on.
We chose every component for one reason: does it capture something a robot needs to know?
Where off-the-shelf hardware is sufficient, we use it. Where it isn't, we're building it. The glove system, the sync hub, and the structuring pipeline are being purpose-built for this problem.
Sensor
What It Captures
Rate
Chest cam
Full egocentric workspace view. What the worker sees.
30fps
Wrist cams ×2
Close-up hand-object contact. Both hands simultaneously.
30fps
Depth cam
3D structure of workspace. Object positions and movement.
Hardware clock pulse. Single timestamp for all streams.
<1ms
All six streams share a single hardware timestamp. No software sync. No post-hoc alignment. Every frame from every sensor is absolutely correlated.
Why Synchronization Matters
One millisecond of error means wrong data.
Five sensors. Five different sampling rates. Without hardware synchronization, you have five independent data streams that cannot be reliably aligned to each other.
Most capture systems use software sync — matching timestamps after the fact. Software sync introduces 5–50ms of jitter. At 30fps, that is 1–2 frames of misalignment. For robot manipulation tasks, one frame of misalignment means the ground truth label is wrong.
Wrong ground truth means the robot learns the wrong thing. We do not allow that.
Central Sync Hub
Hardware clock broadcast · <1ms drift
Vision30hz
Depth15hz
Body60hz
Hands100hz
Force100hz
All streams share one hardware timestamp source. Every frame from every sensor is absolutely correlated. This is not a nice-to-have. It is the foundation of trustworthy training data.
Task Categories
The tasks robots need to learn.
We focus on the physical tasks that appear in homes, hospitals, hotels, and warehouses — the environments where humanoid robots will first be deployed. Every session will be captured in a real environment, not a staged lab.
🧹
Surface Cleaning
Kitchens · Bathrooms · Countertops
👕
Laundry Folding
Multiple garment types · Varied textures
🍽️
Dishwashing
Hand wash · Machine loading
🔧
Basic Repairs
Plumbing · Electrical · Carpentry
🍳
Food Preparation
Chopping · Stirring · Plating
📦
Object Handling
Picking · Packing · Sorting
🛏️
Bed Making
Residential · Hotel rooms
🏥
Clinical Tasks
Sanitizing · Supply handling
Inside One Session
What a single session produces.
A typical session is 6–10 minutes of a skilled worker performing a task in a real environment. What comes out the other end is a structured, synchronized, labeled dataset.
The action primitive layer is what makes this useful for robot training. We don't just record what happened — we record what the robot needs to know about what happened.
Output formats: ROS 2 bags, HDF5, RLDS, and LeRobot format. If your team uses something else, we can support it.
We are building the knowledge base that will teach the next generation of humanoid robots how to do physical work. From India. Honestly. For the long term.
The Founders
Two engineers.
One obsession. Build it.
We looked at the humanoid robot industry and saw something obvious that seemed invisible to most people inside it: every company was racing to build increasingly capable hardware, but the fundamental problem of teaching robots how to do things was barely being addressed.
Robots need to see humans work. Not in labs, not in scripted demos — in real homes, real kitchens, real hospitals. And India is the only place in the world with the combination of trained professional workers, environmental diversity, and operational capability to build that kind of dataset at the scale this industry will eventually need.
That's the gap. That's what we're building. We are at the beginning of it, and we're being honest about that.
Co-FounderRikit Rathi
BEng Mechanical & Mechatronics Engineering (Hons), First Class — University of Hertfordshire. Four years at Cummins UK as a Program Manager — managing complex cross-functional engineering programs, coordinating across manufacturing, supply chain, and product teams.
Brings a systems-level understanding of how hardware programs scale, how physical operations are managed, and how to build infrastructure that works in the real world — not just in a demo.
BTech & MTech in Environmental Engineering from IIT Bombay. Four years at Shell — working at the intersection of large-scale data systems, operational infrastructure, and real-world deployment challenges.
Brings deep technical rigor, experience with industrial-scale data pipelines, and the ability to design systems that are robust enough to run in unpredictable, real-world conditions.
Dexel Labs founded in Bangalore. Began designing the wearable capture system — sensor selection, glove prototyping, synchronization architecture. Building the core technical foundation from scratch.
Now
Developing the capture hardware. Building the team.
We are actively developing our wearable sensor system — the gloves, the sync hub, the full capture rig. Simultaneously building the founding engineering team and formalizing early worker partnerships. We are at the stage where the right people joining now will shape what this becomes.
Next
First prototype validated. First sessions captured.
Complete the first functional capture prototype. Run first field sessions with real workers in real environments. Validate the core technical architecture — synchronized multi-modal capture — in the field.
2027
First dataset delivered. First robotics partner.
Deliver a complete multi-modal dataset to a robotics team. Validate that our data improves robot performance on a measurable task. Become the default source of real-world physical task data for the humanoid robotics industry.
Why India is the right place to build this.
This is not an argument about cost. This is an argument about what kind of data the world's robots actually need.
A robot trained only in American homes, on American kitchens, with American objects — will fail the moment it encounters the rest of the world. The majority of future robot deployments will be in environments that look nothing like a Silicon Valley test lab.
India gives us something no other country can: 1.4 billion people, 22 official languages, radically diverse physical environments, and a workforce of millions of skilled service professionals already performing exactly the tasks robots will need to learn.
The diversity of our data is not a nice-to-have. It is the core of why it will be more useful than anything else in this space.
0People · world's most diverse workforce
0Official languages · no other country matches this
∞Variety of homes, kitchens, workplaces · real-world diversity
0Companies building this from India right now
What We Believe
The things we keep coming back to.
01
Simulation will never fully replace real-world human demonstration.
The sim-to-real gap is real and persistent. There is something about how a skilled human navigates an uncontrolled environment that cannot be reproduced in a physics engine. Real data will always be necessary.
02
The workers who demonstrate tasks are the experts. They deserve to be treated that way.
We don't think of our worker partners as a data source. We think of them as the people whose physical intelligence we are trying to preserve and transmit. Their knowledge is valuable. It should be compensated and respected.
03
Honest data is more valuable than impressive data.
We will not inflate session counts. We will not claim capabilities we haven't built. We will not ship data we haven't validated. A robotics team that trusts our data will rely on us for years. One that discovers we overstated something will never come back.
04
Being from India is a structural advantage, not a cost play.
We are not cheaper. We are different. Our environments, our workers, our linguistic diversity — these produce training data that generalizes to the real world in ways that data from a single culture and geography cannot.
INDIA
What's Next
We are just starting.
If you want to build with us, partner with us, or understand more about what we're doing — reach out.
We don't hire
employees.
We find people
who can't wait.
This is not a job listing. This is an invitation to come build something that has genuinely never been built before — with a small team, in Bangalore, at the beginning of something important.
Rikit & ShivenCo-Founders · Dexel Labs
What a week actually looks like.
No hierarchy. No permission culture. No six-month onboarding. You come in knowing your domain deeply. We point at the problem together. You figure out how to solve it — and you have the space and trust to do that.
Monday
In Bangalore with workers and a sensor kit
Running capture sessions in two homes. Debugging a hardware sync issue that appeared in yesterday's data. Noticing that workers grip differently when they know they're being recorded — and starting to think about how to solve that. Real work. Real problems. No simulations.
Tuesday
On a call with a robotics team reviewing data
Their ML team flags a failure mode — their policy struggles with the wrist rotation during dishwashing. You map the specific failure, design a targeted recapture protocol, schedule new sessions for Thursday. You are directly improving how a humanoid robot learns.
Wednesday
Designing v3 of the glove sensor system
The current prototype is too bulky. Workers change their natural grip wearing it. You're sketching fingertip cap designs, ordering flex sensor samples, running force calibration tests. You are building hardware that will be worn by thousands of workers.
Friday
Reading the ACT paper and writing two paragraphs about it
Understanding how robot companies train their policies tells you what data to collect next. You write a short note on what a new architecture change means for our data format. Everyone on the team reads it. We adjust the next capture protocol accordingly.
What You'll Build
Not features. Not dashboards. Infrastructure.
The first few people who join will design and build the wearable capture system from the ground up. The sensor hardware. The data pipeline. The annotation tooling. The synchronization architecture.
These are not small problems. They haven't been solved at scale before. You will own entire systems — not a corner of someone else's codebase.
And when the capture system works, when the first sessions are running, when a robotics team's engineer tells you their policy improved on a specific task because of data we collected — you will have done something genuinely new.
In your first 90 days:
→ You'll run real capture sessions with real workers in real environments. Not simulations. Not test environments.
→ You'll solve a problem nobody has solved before — whether it's the sync architecture, the glove design, the annotation pipeline, or the data quality framework.
→ You'll see your work used. The output of what you build will be in the hands of robotics teams, and you will see the effect it has on robot performance.
→ You'll shape what this company becomes. Early-stage means the decisions you make about architecture, tooling, and process will be the foundation everything else is built on.
What We Offer
Honest. No fluff.
🌍
Work That Reaches the Physical World
Most software never leaves a screen. What we build ends up in sensors worn by workers, in data consumed by robot AI systems, in robots that operate in people's homes. The impact is physical and measurable.
🧠
You Own What You Build
Early stage means no bureaucracy between your ideas and their implementation. If you propose a better sync architecture, you build it. If you see a flaw in the annotation pipeline, you fix it. Autonomy is real here.
🔧
Hardware + Software + AI
This problem requires all three. You will touch embedded hardware, robotics software, and machine learning in the same week. If that breadth excites you rather than scaring you, you'll fit here.
📍
India Building for the World
Our customers are companies building the most advanced robots ever made. You will have direct conversations with their engineering teams. From Bangalore. That combination — world-class technical work, India base — is rare.
⏰
Now Is the Right Time
Humanoid robots are deploying in homes and factories starting in 2026. The training data infrastructure doesn't exist yet. The window to build it is open now. Joining later won't be the same opportunity.
This is not for everyone.
This is NOT for you if
You want a fully defined job with clear scope
You need management to tell you what to work on next
You're looking for stability over impact
You want a brand-name company on your resume
You're not comfortable with ambiguity and rapid iteration
You want to specialize in one narrow technical area
This IS for you if
You've built something you're genuinely proud of
You look at hard problems and feel urgency
You've stayed up solving a problem you weren't even paid to solve
You want to see the physical effect of your work in the world
The idea of India building something that matters globally excites you
You want to be at the start of something — not the middle or end
Open Roles
We hire builders.
We hire
solvers.
Send proof of work. Tell us what you've built.
Not your CV. Not your LinkedIn profile. What you've actually made work — in the real world, not just on paper.
Hardware · Most Critical
Embedded Systems Engineer
You make hardware come alive. You've written firmware that runs in real time, synchronized sensors to microsecond precision, and shipped embedded systems that survived the real world. At Dexel Labs, you build the synchronization architecture and recording hub that makes our entire capture system possible. If this component doesn't work correctly, nothing else does. This role is the foundation.
Show us what you've built
A sync problem you solved. Firmware that shipped. A circuit or system you're proud of and why. No degree required — proof of work only.
AI · Perception
Computer Vision Engineer
You see the world in tensors. You've built pipelines that turn raw video into structured information — and not just in notebooks, but deployed in production. You turn hours of worker footage into training data that robot AI can actually learn from. This means object detection, pose estimation, multi-camera calibration, and making all of it run reliably at field scale.
Show us what you've built
A CV pipeline that runs in production. A pose model you deployed, not just trained. GitHub links welcomed. Results matter more than papers.
Robotics · Software
ROS 2 Engineer
You speak ROS 2 fluently. You've recorded sensor data into rosbags, fused multiple sensor streams, and built tools that people on robotics teams actually use. The data we deliver needs to plug directly into the training pipelines of serious robotics teams. You are the person who makes sure that works — correctly, consistently, at scale.
Show us what you've built
A rosbag you're proud of and what you built on top of it. What sensors did you fuse? What did you actually deliver — not what you worked on.
AI Research
Robot Learning Researcher
You've read ACT, Diffusion Policy, RT-2 — not as academic exercises, but as specifications for what our data needs to look like. You understand what makes training data useful versus useless from a robot learning perspective. You define what we collect, how we structure it, and whether it will actually make a robot smarter. This role shapes the product more than any other.
Show us what you've built
A paper you implemented. A training run you analyzed in depth. Tell us specifically what you think is missing from the way the industry currently approaches robot training data.
Operations
Field Operations Lead
You've run operations involving real people, real locations, and zero margin for chaos. Not managed — built from scratch and run. You take our wearable capture system into homes, hospitals, and factories, train and manage workers, and ensure every session produces data that's actually worth something. This role is what makes the entire data collection machine work in the real world.
Show us what you've built
An operation you built from nothing. A field problem that broke badly and how you fixed it. We want people who've run things — not people who've managed projects.
APPLY
Still reading?
That's probably your answer.
Send us proof of what you've built. Tell us why this problem matters to you. We'll respond to everything worth responding to.
Each sensor was selected for precision. The integration was built for a purpose no single off-the-shelf kit achieves. Every design decision exists to answer one question: what does a robot actually need to learn?
Vision × 3
Depth
Body IMU
Hand 22DoF
Force
Sync Hub
Timestamp <1ms
ROS2 Output
The Capture System
Five subsystems. One worker.
01👁️
Vision Array
Three synchronized cameras capture the full visual field — what the worker sees, what their hands are doing, and the object they're interacting with.
Instrumented gloves with flex sensors and IMUs across all five fingers — both hands. 22 degrees of freedom at 100Hz. Every grip, every release, every transition.
DoF — 22 per hand · both hands Rate — 100Hz continuous Includes — force at fingertips
03🦾
Body Motion
IMU pods on 17 body landmarks capture full-body kinematics — spine, shoulders, elbows, wrists, hips, knees. Every posture and movement in 3D space.
Fixed depth camera and RGB setup captures the 3D structure of the workspace — where objects are, how they move, what changed during the task.
Depth — RealSense D435 · 15fps RGB — overhead + side angles Output — point cloud + scene map
05⚡
Sync Hub
The most underrated component. A custom recording unit worn on the belt that stamps every data point from every stream with a single shared clock — under 1ms drift.
A session is one worker, performing one task, in one real environment. Every sensor recording simultaneously. A complete, self-contained unit of training data.
Duration6–10 minutes of continuous task performance
Repetitions8–15 full task cycles per session — varied approaches, not scripted
CalibrationIMU suit calibrated per worker per session. T-pose + motion sequence before every capture — eliminates inter-worker drift.
Data volume~18GB raw per session across all 5 streams before compression
Task Categories
Three tasks. Chosen deliberately.
We go deep on three physical task categories before expanding. Each one was chosen because it requires the full sensor stack — and because it directly maps to real humanoid robot deployment goals.
🧹Surface Cleaning
Wiping, mopping, sweeping — requires force feedback (pressure on surface), full arm kinematics, and environmental mapping to track coverage. A benchmark task for force-aware manipulation.
👕Laundry Folding
The hardest mainstream manipulation task. Deformable objects, fine-grained finger control, bilateral hand coordination. Requires all 22 DoF of hand tracking. A key benchmark across Physical Intelligence, Figure, and Agility.
📦Object Packing
Pick, orient, place — into bags, boxes, shelves. Variable object geometry, grasp planning, and spatial reasoning. Real-world variant: hotel room service, warehouse fulfilment, household tidying.
Compact action cameras · both wrists · close-up hand-object interaction. Captures what the chest camera misses during occlusion.
Vision
Depth Camera
Intel RealSense D435 · fixed mount · 15fps point cloud. Maps the 3D geometry of the workspace and tracks object positions during task.
Depth
IMU Suit
17 sensor pods · Rokoko/Noitom compatible · 60Hz. Full body kinematics in global and local reference frames. Joint angles + linear positions.
Motion
Smart Gloves
Custom-built · 22 DoF · flex sensors + fingertip IMUs · 100Hz. Every finger joint angle. Grip type classification included in output.
Hand
Force Sensors
Embedded in glove fingertips. Captures grip pressure and contact force in Newtons. Critical for manipulation tasks — the data most datasets completely miss.
Force
Sync Hub
Custom embedded unit · Raspberry Pi CM4 + custom PCB · hardware-level clock broadcast to all sensors. Drift under 1ms across full session.
Core
Synchronization
Why sync matters.
Five sensors. Five different sampling rates. Five different internal clocks. Without hardware synchronization, you have five independent streams of data that cannot be reliably aligned.
Most data capture systems use software sync — matching timestamps after the fact. Software sync introduces 5–50ms of jitter. At 30fps that's 1–2 frames of error. For robot manipulation tasks, 1 frame of misalignment means wrong ground truth.
We broadcast a hardware clock pulse to every sensor simultaneously. Every data point across all five streams shares a single timestamp source. Drift under 1 millisecond — guaranteed.
Central Sync Hub
Hardware clock broadcast · <1ms drift
Vision30hz
Depth15hz
Body60hz
Hands100hz
Force100hz
All streams share a single hardware timestamp. Misalignment <1ms across an 8-minute session. Your training pipeline can trust every frame correlation absolutely.
Data Pipeline
Raw sensors → robot-ready data.
1
Capture
Worker performs task. All 5 sensor streams record simultaneously. Sync hub timestamps everything. Raw data saved to local SSD.
2
Ingest
Session data uploaded to cloud pipeline. Integrity checks run automatically. Incomplete or corrupted sessions flagged for recapture.
3
Label
Annotation team segments actions into primitives. Grasp type, object identity, surface type, force magnitude, task phase — all labeled.
4
Structure
Labeled data converted to robot state/action format. Observations and actions aligned to standard robot learning schemas.
5
QA
Automated quality checks + human review. Sync integrity, label completeness, sensor coverage, outlier detection. Reject rate tracked per session.
6
Deliver
Packaged as ROS 2 bag + HDF5 + RLDS. Client downloads via secure link. Integration docs included. Format customization available.
7
Feedback Loop
Your robot fails on a task. You flag the failure case. We identify the data gap, run targeted recapture sessions, and deliver a supplementary dataset within days. The dataset improves with the robot.
Output Formats
Your pipeline. Your format.
We deliver in whatever format your training pipeline consumes — natively. No reformatting. No preprocessing. Your engineers open the files and start training.
If your team uses a format not listed here — tell us. We will support it. Our job is to make integration zero-friction.
ROS2
ROS 2 bag files — native format for every robotics team. Plug directly into your ROS2 pipeline. All sensor topics included.
Default
HDF5
Hierarchical Data Format — standard for ML training pipelines. Time-aligned arrays. Ready for PyTorch / JAX dataloaders.
Default
RLDS
Reinforcement Learning Dataset — Google's standard. Compatible with Open X-Embodiment and RT-2 training pipelines directly.
Available
LeRobot
HuggingFace LeRobot format — for teams using the open-source robot learning ecosystem. Drop-in compatible.
Available
Custom
Your proprietary format, your schema. We write a custom exporter for Enterprise contracts. No extra cost.
Enterprise
Why this is hard to copy.
01
Hardware Sync at Scale
Getting sub-millisecond sync across 5 sensor modalities is a non-trivial embedded systems problem. It took us months to solve reliably in field conditions. A competitor can buy the same sensors. They can't instantly replicate our sync architecture.
02
Worker Network at Scale
The technology is one part. The other part is having hundreds of trained workers in real environments willing to wear the kit and perform naturally. That trust, that network, that operational infrastructure — takes years to build. We are building it now.
03
Proprietary Structuring Layer
Raw sensor data is not training data. Our action primitive extraction pipeline — how we convert human motion into robot-ready observations and actions — is our core IP. It is not a standard tool. It is something we are building and refining with every dataset we deliver.
04
Calibration Infrastructure
Inter-worker variability is the silent killer of multi-worker datasets. Every worker, every session: a full IMU calibration sequence before capture begins. T-pose, range-of-motion sweep, grip baseline. The same worker performing the same task twice produces comparable data. That consistency doesn't happen without process.
TECH
Ready to integrate?
See the data in your pipeline.
Request a sample dataset. Your engineers test it. Zero commitment. If it works — we talk scale.