The Skills Layer for Physical AI

Commercial robots need deployable skills.

Robot manufacturers deliver the hardware. Foundation models provide general intelligence. Ergodexa delivers production-ready Skill Packs that enable robots to perform specific jobs in warehouses, hotels, factories, hospitals, and beyond. Every skill is validated on commercial robot platforms and continuously improved through real-world deployment.

Choose the robot. Add the skills. Put it to work.

An Ergodexa-branded humanoid robot
75,000Hours of task capture
1,028Skills in the registry
3.7MAnnotated segments
12GR00T-ecosystem platforms
Our Approach

The Skills Layer Above the Foundation Model

Foundation models such as GR00T and π0 provide robots with general perception, reasoning, and manipulation. They enable a robot to attempt many tasks, but they do not produce deployable workers that consistently perform a defined job to an operational standard.

That is the missing layer we build.

We transform general-purpose Vision-Language-Action models into production-ready job skills — restocking shelves, turning hotel rooms, tending CNC machines, inspecting infrastructure — using task-specific training data, specialized policy tuning, and objective performance benchmarks on the hardware customers already deploy.

The result is not another foundation model. It is a library of commercially deployable robot skills that deliver measurable labor outcomes.

01 / Data

We specify the data

Every skill begins with a data specification: hours of human egocentric footage, teleoperated demonstrations on the target embodiment, sensor coverage, and how much environment variety the policy needs to generalise. We then source what already exists and capture what does not, blending licensed libraries, purpose-built capture programs and simulation rollouts.

02 / Models

We train the policies

We post-train GR00T on that corpus rather than starting from scratch, so each policy inherits broad manipulation priors before it ever sees your task. Reinforcement learning in simulation pushes it past the demonstrations it learned from, and domain randomization plus on-site fine-tuning harden it for the variability of real rooms.

03 / Deployment

We deploy across embodiments

A hardware abstraction layer carries the same policy across certified platforms. Skills arrive as versioned packages, updated over the air, measured against acceptance gates.

Build the business case before you buy a robot.

Our Deployment Impact Assessment baselines the labor in scope, quantifies what automation is worth on your floor, and returns a costed plan your finance team can underwrite.

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