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
  1. You set the goalA research goal, your robot or simulator, and your limits.
  2. It runs the researchHypotheses, code, experiments, diagnosis, next steps.
  3. It evolvesWhat it verifies, it keeps. Every task starts further ahead.

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.

Every experiment and every paper reshapes how it does research next. It doesn't install new methods. It integrates them.

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 four steps.
  1. 1

    Relate

    Connects it to its own experience and your research: which past failures it could explain, which skills it touches.

  2. 2

    Adapt

    Rewrites it for your robot, your data and your limits, instead of copying the paper's setup.

  3. 3

    Test

    Checks it against a fixed test the agents cannot edit, on trials it has never seen.

  4. 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.

  • Capabilities

    Skills, tools and models. 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 your research history.

You bringIt changes
  • A world-model paperHow it plans experiments: candidates are screened in the model first, so fewer robot trials.
  • A perception repositoryIts pose estimation, inside the skills it already has.
  • “Prioritize safety near the fixture”Its strategy and limits, on every later task.
  • A new robotIts skills, re-tested on that robot before use.

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. 1

    Your research partner

    Evolves around one researcher: your goals, your methods and the way you work.

  2. 2

    Specialized around a robot

    Builds up one robot's skills, tools and research history, so every new task starts further ahead.

  3. 3

    A team of research agents

    Specialized research agents coordinate across a team and share what they verify.

Self-evolving AI research system for robotics.

Every task, every experiment and every paper makes the next one faster.