Self-evolving AI research system for robotics.

Give it a research goal, your robot or simulator, and your constraints. It runs the experiments, learns from every result, and keeps getting better at research.

Watch it work How it evolves

Experimenting Capabilities 33

You set the directionGoals, robots, your workflow and your judgment.
It researches and internalizesExperiments, new methods, and the simulated worlds to test them.
It reports back and grows with youWhat it found, what it kept, and what it needs from you.

Watch it work.

One research session: a task, a harder task that builds on it, and a new method discussed with the researcher and integrated into the whole system.

Task 1 Thread the needle

Simulation · one arm

Experiments to a verified result

Task 131
Task 214
Task 36

Capabilities 30 → 34

0:00 / 0:00

It learns how you research. Then it learns to research better.

It learns from the way you work with it, from its own experiments and from the field. What a task needs, it finds or builds: a method, a tool, a simulated world to test in. What proves better, it keeps.

How you workYour workflow, your decisions and corrections, and how you divide work between agents.

Its own experienceEvery experiment, every failure and every fix it has verified.

The fieldPapers, repositories and methods it finds, or that you point it to.

1
Relate
Connects what's new to its own experience and to how you work: which past failures it explains, which skills it touches.
2
Build
Makes whatever the work needs. It adapts methods to your robot and writes the tools, and if there is no world to test in, it builds one.
3
Test
Checks the result against a fixed test the agents cannot edit, on trials it has never seen.
4
Integrate
Changes the whole system: skills improve, old conclusions get reopened, your way of working becomes a workflow it keeps. What doesn't help is left out, with a reason.
5
Report back
Tells you what changed and what it left out, and asks when a decision is yours. Your answer becomes part of how it works.

Your workflow
How you run research and work with agents: the checks you make first, the calls you make, the corrections you give.
Capabilities
Skills, tools, models, methods from the field and the simulated worlds it builds. Verified ones are kept and reused.
Research history
Every experiment and what it showed. Old conclusions are reopened when something new explains them.
Research strategy
How it picks the next experiment. Changes are measured against predefined criteria before they are adopted.

You bringIt brings back

How you run a studyPlan before building, a separate agent for testing, negative results kept apart. Every later task follows the same workflow without being told again.
A new task with no simulatorThe simulated world it built for that task, and the checks that show the world can be trusted.
A paper or a repositoryThe method rebuilt inside the skills it already has, with the test that shows the gain. What didn't help is left out, with the result that ruled it out.
A new robotWhich skills carry over, re-tested on that robot.
Your answer to its questionIt asked: “Check perception first on every task?” You said: “Yes, for insertion tasks.” From then on, that is how it works.

It follows your preferences. It proves everything else first.

A research system that grows with you.

Each Hephorvis evolves around its people, its robots and its history.

1
Your research partner

Learns how you work: your goals, your methods, your judgment. You steer it, and it brings back what it finds.
2
Specialized around your robots

Builds up your robots' skills, tools and the simulated worlds they need, so every new task starts further ahead.
3
A team of research agents

Specialized agents coordinate across a team and share only what they verify.
4Long-term vision
Your own research superintelligence

Beyond robotics: one system that keeps learning from all the research you do, and stays yours.