AI Models & Trust

ROBIN uses a PyTorch MLP (ProcessGeometryMLP) to predict process geometry from process parameters, or to suggest parameters for a target geometry. Models are managed through the dashboard and the REST API.

Model Control in the Dashboard

Navigate to the Models & Trust tab in the sidebar.

Active Model

The top card shows the currently loaded checkpoint - name, file path, and size. The model is loaded at startup from the path specified in the active profile’s ai.model_path field.

Checkpoint Registry

All .pt files found under data/models/ are listed with their size and modification date. Click Load to switch the active model at runtime.

Quick Prediction

Enter the process parameters (e.g. speed, current, voltage), then click Predict Geometry to run a forward pass and see predicted height and width instantly.

Model Routing

ROBIN supports position-based routing: assign different model checkpoints to robot positions PA and PC. This lets you run specialised models per station.

Trust Thresholds

Set the Warning and Stop confidence levels. When the runtime trust score for a robot drops below a threshold, the corresponding safety gate fires:

• Warning - operator is alerted, robot continues

• Stop - robot pauses automatically

API Endpoints

Endpoint	Description
GET /ai/models	List all available model checkpoints
GET /ai/models/active	Get the currently loaded model
POST /ai/models/select	Load a specific checkpoint by path
POST /ai/models/predict	Run a forward prediction with given parameters

Training a New Model

python scripts/train_profile_model.py

This script:

1. Generates synthetic training data using a physics-inspired simulator

2. Trains a ProcessGeometryMLP network (configurable hidden layers, dropout)

3. Saves the checkpoint to data/models/<profile>/process_geometry_mlp.pt including feature normalization statistics

Each profile specifies its own ai.model_path, so training a new model for one profile does not affect another profile’s checkpoint.

Model Architecture

ProcessGeometryMLP maps 3 input features to 2 outputs:

• Inputs: 3 process parameters (order defined by ai.feature_order in the profile YAML - e.g. speed, current, voltage)

• Outputs: predicted height, predicted width

• Normalisation: per-feature mean/std stored in the checkpoint

• Configurable: number of hidden layers, hidden size, dropout rate