We help manufacturers automate dexterous tasks by recording how their best workers perform them — and using that data to train robots.
Our EMG sleeve captures physical information that cameras miss.
Individual finger movements and fingertip pressure, tracked in real time from electrical signals in the forearm. No cameras, no gloves, just a sleeve.
Watch on LinkedInValidated against Apple Vision Pro hand tracking on held-out recordings. After re-donning the sleeve, two minutes of movement re-calibrate the model in under a minute.
Apple Vision Pro hand tracking on the left. On the right, the pose decoded live from forearm EMG alone. The decoder was trained four days earlier, on a separate wearing of the sleeve, and never recalibrated.
Mean absolute error over all 20 joint angles and every headset frame of the 215-second clip; holding one pose would score 15.7°. Correlation is Pearson r over the 17 joints that move. The decoder trails the headset by about 100 ms, the offset you can see. Rendered from the recorded data.
No cameras. The full-sleeve EMG and the model behind it move the hand as the human does, in real time.
Watch on LinkedInTwo films as posted on LinkedIn. The headset comparison is rendered from our own recording.
Video tells you where the hand moved. EMG gives another signal for what the human was physically doing to make that movement happen — muscle activation, inferred effort, the preparation before contact. We measure further upstream in the control signal than a glove, and the hand stays completely free.
Recorded forearm EMG · 32 ch · 6 repeats · 2,048 Hz
Your best workers do the task wearing our EMG sleeve, on camera, with hand tracking. The sleeve leaves the hand completely unobstructed.
EMG, video, and hand pose are aligned into a task-specific dataset — including the muscle activation and inferred effort cameras miss.
Demonstrations aren't copied one-to-one. They're retargeted into the robot's action space to train a policy for your task, using physics AI models we developed in-house.
The robot runs on its own sensors — it never wears the sleeve. The EMG guides training, then drops away.
01
Manufacturing · Italy
A European manufacturer identified delicate, force-sensitive sewing as one of its hardest automation problems — and signed an LOI for us to support robotic implementation.
02
Construction · Pilot
We're also on construction sites, recording electricians, plumbers, and demolition crews — completely different work, captured with the same sleeve.
We started with sewing because it's brutally hard for a robot: the fabric stretches, the grip changes constantly, and one clumsy move ruins the piece.
Crack that, andeasier tasks follow.
Fabric, cables, gaskets — material that moves when you touch it and punishes a clumsy grip.
Rhythm / adaptationEvery skill leaves a different signal · illustrative traces
For robotics and AI labs — generalised datasets of human physical interaction, recorded during real production work: EMG, video, and hand pose, aligned.
Get in touchCEO & Co-Founder
Oxford MBiol research — signal processing · EMG calibration paper in preparation (1st author)
Built with a hardware company from the ground up · Seen a unicorn scale from the early days
CTO & Co-Founder
Oxford medic — research in machine learning and AI in cancer and stroke imaging
Published multiple papers · Repeat founder — medical LLM used at scale · Worked on Oxford's first quadruped robot · Robotics national finalist
Advisors
Manufacturers, robotics teams, researchers — or anyone who wants in early. Tell us what you're working on and we'll be in touch.
