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Once a training run is marked Done, the model is ready to download to your robot and use as a skill.

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Download the model

When a run completes, the trained checkpoint is downloaded to the robot and activated.
A completed run appears in the Completed tab (or shows a download button in the Runs tab).
1

Open the completed run

Navigate to the skill page and open the Completed tab. Find the run you want to deploy.
2

Tap Download

Tap the download button on the run card. The app downloads the trained checkpoint and dataset statistics file to the robot.
3

Wait for activation

A progress bar shows the download and verification stages. When it completes, the model is automatically activated — the skill’s metadata.json is updated with the checkpoint path.
The robot’s brain reloads automatically after activation. Your skill is now live.
Auto-download is also enabled. If the robot is on and connected when a training run finishes, the model downloads and activates without any manual action.
When a model downloads, the robot pre-builds an optimized TensorRT engine for it, so policies run fast on-device with temporal ensembling for smoother motion. This happens automatically — there’s nothing to configure.

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Run the skill from the app

The web app doesn’t run skills — use the phone app’s Manual Control (below), or call the skill from code.
The simplest way to test your trained skill is from Manual Control.
1

Open Manual Control

Go to the Manual Control screen from the app’s main navigation.
2

Select the skill

Open the skill dropdown and select your newly trained skill. Only activated (non-training) skills appear in this list.
3

Execute

Tap the play button. The robot moves to the start pose and begins running the policy at 25 Hz — reading cameras, processing images, and outputting arm and base commands in real time.
4

Stop if needed

Tap the stop button at any time to interrupt execution. The robot halts immediately.

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Run the skill from code

Trained skills are available to agents and code-defined skills just like any other skill. Reference the skill by its ID in your agent’s skill list: 
from brain_client.agent_types import Agent
from typing import List

class TidyUpAgent(Agent):
    @property
    def id(self) -> str:
        return "tidy_up"

    @property
    def display_name(self) -> str:
        return "Tidy Up"

    def get_skills(self) -> List[str]:
        return ["pick_up_cup", "navigate_to_position"]

    def get_prompt(self) -> str:
        return """You are a tidying robot. Use pick_up_cup to grab cups
        you see, then navigate to the kitchen to put them away."""
The Innate agent calls your trained skill the same way it calls any other skill — the execution pipeline handles loading the checkpoint, running inference, and sending commands to the hardware.

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What happens during execution

When the skill runs, the BehaviorServer loads the checkpoint, moves the arm to the learned start pose, then enters a 25 Hz inference loop: it reads both cameras and the joint state, runs the policy, and streams arm and base commands until the task completes. The step-by-step breakdown lives in Policy-Defined Skills.

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Multiple training runs

You can train multiple runs with different hyperparameters for the same skill. Each run produces an independent checkpoint stored in its own subdirectory. When you download and activate a run, it becomes the active checkpoint for that skill. To switch between runs, download a different completed run — activation overwrites the checkpoint path in metadata.json.

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Iterating on a skill

If the skill doesn’t perform as expected:
  • Add more episodes to your dataset, sync again, and retrain. More data almost always helps.
  • Adjust hyperparameters — see the training guide for tuning advice.
  • Review your demonstrations — replay episodes to spot inconsistencies, then re-record the weak ones.
Training is fast and cheap to iterate on. Don’t hesitate to run multiple rounds.