> ## Documentation Index
> Fetch the complete documentation index at: https://docs.orchestrallabs.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Isaac05 quickstart

## Isaac 0.5 Quickstart

This guide deploys the public [PerceptronAI/Isaac-0.5 checkpoint](https://huggingface.co/PerceptronAI/Isaac-0.5)
through Servo's hosted model path. It assumes you have completed the [Servo quickstart](/quickstart)
through robot enrollment, signed in with `servo login`, and connected your Hugging Face account.

Servo has a checkpoint-native Isaac 0.5 runtime. You pass the Hugging Face repository to Servo;
you do not need to clone the Perceptron inference repository or install its model dependencies on
the robot computer. Servo pins the released checkpoint revision below so the inspected inputs and
the deployed model refer to the same files. This is a large, high-memory policy, so deployment
depends on suitable hosted capacity.

## Inspect the checkpoint

Inspect the pinned checkpoint revision and read Servo's reported input contract.

```bash your computer theme={null}
servo model inspect hf://PerceptronAI/Isaac-0.5 \
  --revision 6e397429f0ab91528ebac915004eba5e763fcdd4
```

The checkpoint's input contract is two RGB camera images and an eight-value state vector. For this
revision, Servo reports `observation.images.image` and `observation.images.wrist_image` as
`uint8` images shaped `256 × 256 × 3`, and `observation.state` as `float32` shaped `[8]`.
Use the inspection output as the authority if you choose a different revision.

## Import the checkpoint

Import the pinned checkpoint from Hugging Face into your Servo organization. This downloads and
prepares the model; the command waits for it to become ready by default.

```bash your computer theme={null}
servo model import hf://PerceptronAI/Isaac-0.5 \
  --revision 6e397429f0ab91528ebac915004eba5e763fcdd4
```

Copy the imported model ID from the command output. You can find it again with
`servo model list`.

## Deploy near your robot

Deploy the imported model near the enrolled robot. Isaac 0.5 is a large, high-memory policy, so
deployment depends on suitable hosted capacity and its initial startup takes longer than a small
checkpoint.

```bash your computer theme={null}
servo model deploy mdl_YOUR_IMPORTED_MODEL_ID --robot robot-1
```

Save the deployment ID printed by Servo. Wait for the deployment to report `active` before sending
predictions. Replace `robot-1` with the name or ID of your enrolled robot.

## Send the first observation

Use real sensor arrays with the names and shapes from inspection; do not send test data to a physical robot.

```python isaac_first_prediction.py theme={null}
import numpy as np
import servo

sv = servo.Servo()
deployment = sv.deployments.get("dep_YOUR_DEPLOYMENT_ID")

inputs = {
    "observation.images.image": np.load("image.npy"),
    "observation.images.wrist_image": np.load("wrist_image.npy"),
    "observation.state": np.load("state.npy"),
}

with deployment.session(
    instruction="pick up the object and place it at the target",
) as session:
    prediction = session.predict(inputs=inputs)
    print(f"Actions: {len(prediction.actions)} steps x {prediction.action_dim} values")
```

The arrays must match the names, shapes, and dtypes reported by `servo model inspect`. Connect the
returned action chunk to a robot-side controller that understands Isaac's action convention and
control rate; Servo does not infer a robot-specific calibration from the checkpoint.

## Stop the deployment

Stop the deployment after use:

```bash your computer theme={null}
servo deployment stop dep_YOUR_DEPLOYMENT_ID --wait
```

The imported model remains in your organization for later deployments. For model details and the
checkpoint's license and reference implementation, see the [Hugging Face model card](https://huggingface.co/PerceptronAI/Isaac-0.5).


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