디자인 훈련 배포
- 시각적 워크플로우 블록
- 내장 AI 어시스턴트
- 온라인 훈련 및 재생
로봇 훈련이 이렇게 간단한 적은 없었습니다.
From a robot model to a running policy
Four steps, one browser tab. You install something only when you want the training to run on your own machine.
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01
Bring a robot
Start from one of the built-in robots, or import your own description and edit it in the browser.
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02
Compose the graph
Six nodes carry the whole configuration — robot, observation, environment, task, checkpoint, trainer.
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03
Train
On our cloud GPUs, on your own server, or on Isaac Lab on the machine under your desk.
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04
Check it, then take it
Replay the trained policy in MuJoCo, then export a bundle with the contract a robot needs to run it.
AI에게 말하세요
AI가 설정을 구축합니다.
컴퓨터에서 실행 중인 AI를 가리키세요. 또는 API 주소를 가리키세요. 아니면 당사의 에이전트를 사용하세요.
코드 대신 블록
이전에 코드로 작성했던 모든 것이 이제 블록입니다. 드래그하고 연결하면 끝입니다.
Bring your own robot, and your own ground to walk on
Two full editors, in the same tab as the canvas. Your files are read where you dropped them — a description you upload is parsed in your own browser, not on a server.
Robot Editor
Drop in a robot description with its meshes and it opens as an editable model, link by link and joint by joint.
- URDF and MuJoCo XML are parsed in the browser; a USD stage is read by our service
- Assign joint roles, check inertials, set the actuator model
- Every value carries where it came from — yours, estimated, or a default we injected
Scene Editor
Author the terrain the policy learns on, with a brush and a set of generators — then use it in a training canvas.
- Sculpt the ground, or generate stairs, slopes, boxes and rough terrain
- Place props that really collide, and set where the robot spawns
- Saved into your own library, ready for the environment node
Import what you already have
Four kinds of asset, and more than one way in. Everything you import lands in your own storage, under your own account.
From a public repository
Paste a GitHub link, pick the files, confirm. A robot description brings the meshes it names along with it.
From your own machine
Upload a file, or let the local helper copy a model, a scene, a clip or a finished run in from your own computer.
From the community
Subscribe to something another user published — it installs into your library ready to use.
어디서나 트레이닝
온라인 시뮬레이션
我们的 GPU / 你的云服务器 或 你自己的机器。
아이디어에서 훈련된 로봇까지
모든 비용을 통제하세요. 완전 무료로 시작하세요. 강요하는 월간 구독은 없습니다.