One window.
Every step.
Record, label, train, infer, fork, publish — all behind a single sidebar. No half-dozen browser tabs. The seam between cloud and local is invisible.
One window. Every step.
Record, label, train, infer, fork, publish — all behind a single sidebar. Click through the workspace. Compatibility checks, latency budgets, and per-backbone envs are baked in.
Recent activity
One pipeline. Any foundation model.
AXION is built backbone-agnostic from day one. Same dataset, same UI, same fork tree — pick the foundation model that fits your task. New backbones add via yaml, not code.
New backbone = new yaml. Zero code changes.
Every backbone declares its env, weights, runner, and what it supports. Add a yaml, AXION enumerates it in the picker, validates compatibility, and pulls a conda-pack tarball on demand.
name: grootn17 display: "GR00T N1.7" version: "N1.7-3B" conda_env: axion_grootn17 runner: axion.training.runners.grootn17 supports: skill_types: ["standard", "advanced"] cameras: ["wrist", "topview"] os: ["linux", "windows-wsl2"] default_config: skill: { strength: 16, lr: 5e-5, epochs: 15 } # jayden builds the env once. conda-pack uploads # a tarball. user downloads it. no pip wars.
Cloud trains it. Your robot runs it.
Training is heavy and intermittent — perfect for managed cloud GPU. Inference is real-time and continuous — perfect for the GPU already on your desk. AXION orchestrates both.
Captured episodes
Teleoperated demos, captured locally in the AXION app.
Managed cloud skill build
Container image axion-pi05:1.0 on a managed GPU node. 2–7 hours per skill. Pre-emption safe.
Local 50ms control
Your skill hot-swaps onto the cached base. Control loop stays under 50ms, frames never leave your network.