Data can’t just be outsourced🤯 To iterate fast, robotics teams must own their data infrastructure
Introducing SyncField: turnkey data infrastructure for in-the-wild data collection (Best for UMI-style & Embodied human) #Robotics#UMI#DataCollection
Physical AI needs human data, but human data capture is still way too hard.
Not because pressing record is hard.
Because the moment you add cameras + sensors, everything gets messy:
Every device has its own clock.
Streams can silently fail.
Recording health has to be checked.
Start / stop has to line up.
Synchronization has to be solved after.
SyncField Desktop turns it into one workflow.
Auto-discover cameras + sensors.
Connect streams with aliases.
Drag, arrange, and monitor panels.
Record everything in one click.
Review synchronized playback.
Get frame-aligned data on disk.
No handclaps. No LED flashes.
No sync scripts. No file wrangling.
Just humans doing real tasks, captured cleanly.
If you're working on human data for Physical AI, reach out:
https://t.co/MR4Mljaut5
world models aren't just bigger video models
What we truly need: (1) multimodal environments (2) structure-based reasoning (geometry, physics, affordances, spatial & symbolic reasoning) (3) Physics-aware interactions (4) Continuous real-world data loops
data is being collected in regions where robots won’t be deployed anytime soon due to low labor costs, while the environments where deployment is actually viable remain largely inaccessible and require smarter, more strategic approaches to unlock
Robotics has spent decades optimizing for research. Deployment requires a completely different kind of person: operators, industrialists, and outsiders the field typically ignores.
There's a wave of people who want to build in robotics. The field doesn't know what to do with them.
New essay, Robotics Needs Fewer Roboticists* below 👇
Excited to share that @OpenGraph_Labs has been accepted into @NVIDIA’s Inception Program 🚀
Our mission is to build reliable infrastructure for multimodal data capture, powering the next generation of robotics & world models 🌎
World models can predict the next frame.
They can't predict the next touch.
That's the gap visuo-tactile world models will close.
Is the robot gripping hard enough?
Is the surface rigid or soft?
When exactly does contact begin and end?
Vision doesn't know. Tactile does.
We built @OpenGraph_Labs to capture what cameras miss.
Egocentric RGB × 5-finger multi-taxel tactile gloves.
Frame-synced. Calibrated. In-the-wild.
No lab setups. No scripted pick-and-place.
Just humans doing real tasks in real stores.
Watch the exact moment contact happens.
The pressure map lights up in sync. Every touch. Every frame. 👇
Yeah, that’s true. Their gloves and the data collected from them are compatible with their robots.
I’m also betting on human data but only when it’s captured as high-quality multimodal data. What I’m working on is a multimodal data capture tool that helps collect high-quality, in-the-wild data while keeping different sensors time synced and handling issues like sensor drift automatically
Robotics & world models require real-world multi-sensory data at scale.
But collecting vision, tactile, and IMU data simultaneously is much harder than it sounds. Each sensor runs at different frequencies, latencies, and clock domains. Integrating them means dealing with hardware quirks, driver inconsistencies, and constant timestamp drift.
This is fundamentally a synchronization problem. And it gets harder as more modalities are added and tasks become longer-horizon, because temporal misalignment compounds: the model loses the causal structure of what happened and when.
We learned this the hard way building our own pipelines. That experience led us to build a unified platform for multimodal capture, one that handles time alignment, hardware abstraction, and data integrity from day one.
@OpenGraph_Labs built 'SyncField - Multimodal Data Capture System " which:
▪️ Supports any hardware configuration (multiple cameras + tactile + IMU)
▪️ Automatic synchronization across all modalities
▪️ Output is fully time-aligned and ready to train on
It already powers humanoid robotics teams, data collection companies, and university research labs. If your team is collecting multimodal robotics data, we'd love to talk. (now onboarding teams one by one)