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, papers 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, a paper discussed with the researcher, and the paper 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 the way researchers do, with you.

Every experiment and every paper reshapes how it does research next. It doesn't install new methods. It integrates them, then tells you what changed and asks when a decision is yours.

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

New research you bringA paper, a repository or one sentence of direction.

One processBoth go through the same steps, and back to you.

1
Relate
Connects it to its own experience and your research: which past failures it could explain, which skills it touches.
2
Adapt
Rewrites it for your robot, your data and your limits, instead of copying the paper's setup. If it has no world to test it in, it builds one.
3
Test
Checks it 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, the research strategy shifts. 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.

Capabilities
Skills, tools, models and the simulated worlds it builds. Verified ones are kept and reused; each version stays where it works best.
Knowledge
Methods from papers and repositories, adapted and tested before they are trusted.
Research strategy
How it picks the next experiment. Changes are measured against predefined criteria before they are adopted.
Fit to you
Your workflows, your robots, your preferences and every decision you make with it.

You bringIt brings back

A world-model paperWhat it kept: candidates screened in the model first, so fewer robot trials. What it left out: the paper's planner, with the result that ruled it out.
A perception repositoryIts pose estimation, rebuilt inside the skills it already has, with the test that shows the gain.
“Prioritize safety near the fixture”A changed strategy and limits on every later task, and what that trade-off costs.
A new robotWhich skills carry over, re-tested on that robot, and the simulated world it built to test them.
Your answer to its questionIt asked: “Screening in the model saves robot trials but costs compute. Use it by default?” You said: “Yes, except near the fixture.” From then on, that is how it works.

It changes itself, but only what proves better.

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.