Isaac 0.5

Checkpoint format for fine-tunes

Isaac 0.5 is imported as a complete Hugging Face snapshot because the policy weights, Qwen language model, image processor, and LeRobot policy processors use relative paths. Do not upload only the policy directory or only the changed weight shards. Preserve the release’s root-relative layout and update the metadata to describe the fine-tuned weights. The snapshot must include:

Path	Purpose
`config.json`	Isaac recipe and relative paths (`hf_model_path: ".."`, `fast_processor_path: "../fast_processor_pinned"`)
`model.safetensors.index.json` and every referenced shard	Qwen model weights
`lerobot_policy/config.json`	Policy dimensions, cameras, chunk, observation history, and target rate
`lerobot_policy/policy_preprocessor.json` and `policy_postprocessor.json`	Matching input and output processors; the preprocessor includes the enabled Isaac Harmony pack
`fast_processor_pinned/processor_config.json` and `tokenizer_config.json`	Pinned image and language preprocessing configuration
Remaining files referenced by those configs	Policy weights, tokenizer assets, and processor state

The camera order, image size, action and proprioception dimensions, chunk size, number of observation and action steps, dtype, inference backend, and target rate must agree between the Isaac recipe and its processor. Servo validates that agreement during inspection/import. Keep the whole snapshot in one repository revision so relative references cannot resolve to mismatched files.

Inspect the input contract

Inspection reads metadata and small config files before deployment; it does not download the model weights. For the public revision, the input contract has two image arrays named from its camera order (observation.images.image and observation.images.wrist_image) and an eight-value observation.state array. Check your own fine-tune’s result because its dimensions may change. Run the SDK inspection to print the checkpoint’s input keys and shapes.

inspect_checkpoint.py

import servo

sv = servo.Servo()
inspection = sv.models.inspect_checkpoint(
    "hf://PerceptronAI/Isaac-0.5",
    revision="6e397429f0ab91528ebac915004eba5e763fcdd4",
)
for item in inspection.input_contract.inputs or ():
    print(f"{item.name}: {item.shape} ({item.modality})")

Deploy

Deploy the pinned public checkpoint to the robot registered with your organization:

your computer

servo model deploy hf://PerceptronAI/Isaac-0.5 --revision 6e397429f0ab91528ebac915004eba5e763fcdd4 --robot robot-1 --wait

Import and deploy a fine-tune

Import the fine-tune at the same pinned revision you inspected, then deploy the returned model ID:

your computer

servo model import hf://ORG/REPO --revision COMMIT
servo model deploy mdl_YOUR_MODEL_ID --robot robot-1 --wait

The public checkpoint can be deployed directly from its hf:// path as shown above. Preserve the entire fine-tuned snapshot and import one pinned revision so its relative weight and processor paths stay aligned.

Send observations and receive actions

On the robot computer, construct Session.predict(inputs=...) with the exact keys, shapes, and dtypes from the deployed policy. The quickstart prints those resolved inputs and shows the shared prediction flow.

Get started

Guides

Reference

Checkpoint format for fine-tunes

Inspect the input contract

Deploy

Import and deploy a fine-tune

Send observations and receive actions

​Checkpoint format for fine-tunes

​Inspect the input contract

​Deploy

​Import and deploy a fine-tune

​Send observations and receive actions

Checkpoint format for fine-tunes

Inspect the input contract

Deploy

Import and deploy a fine-tune

Send observations and receive actions