Robot intelligence that learns new work and keeps it.

Robot intelligence that learns new work from demonstrations, adapts as the work changes, and carries across robot platforms.

Build with us Try the playground

Traditional automation works — until the work changes.

New products, objects, layouts and workflows still trigger expensive engineering cycles. We are building an automation layer where robots learn from demonstrations and adapt as the work changes.

01
New product
A new part, object, layout or procedure arrives.
02
Show the task
Teach the work through demonstrations instead of engineering every behavior from scratch.
03
Adapt
The model adapts across objects, positions, environments and workflows.
04
Run
The robot does the work, with no new cycle of data collection, retraining and reprogramming.

Automation should not have to start over every time the world changes.

Built for

Precise
Assembly, insertion, cable handling, tooling and contact-rich manipulation.
Multi-step
From isolated robot skills to complete physical workflows.
Robust
Operating through variation, disturbances and imperfect real-world conditions.
Responsive
Bringing frontier models toward fast, closed-loop physical control.

The model we are building now.

Vector

Research

Adaptive intelligence for precise manipulation.

Learns from robot demonstrations, handles variation in objects and scenes, and is built to transfer across robot systems. The current Vector model runs live in the playground.

Adaptation
Precision
Robustness
Cross-embodiment

Try it in the playground

Not locked to one robot.

Intelligence should carry across arms, grippers, cameras and mobile bases, so a skill learned once is not rebuilt for every machine.

Arm
Gripper
Camera
Mobile base

Available

Run the current model yourself.

Arrange a scene in your browser and watch the current Vector model attempt the task, with its real success rate shown.

Open the playground

Build with Robotensor

We collaborate with robotics companies, research labs, and industrial partners on robot learning, simulation, data collection, and evaluation.