dora/node-hub/dora-policy-inference

• dora-policy-inference
• • Quick Start
  • • 1. Installation
    • 2. Usage Guide
    • 3. Start Policy Inference
  • Configuration
  • • Required Environment Variables
  • Camera Configuration
  • Model Requirements
  • • • Device Selection
  • License

dora-policy-inference

Load pre-trained policies and execute them in real-time to control robots based on camera observations and current robot state.

Quick Start

1. Installation

# Source your venv 
cd dora/node-hub/dora-policy-inference
uv pip install -e .

2. Usage Guide

Create a dataflow file, see examples/lerobot-dataset/policy_inference.yml:

nodes:
  # Policy inference
  - id: policy_inference
    build: pip install -e ../../node-hub/dora-policy-inference
    path: dora-policy-inference
    inputs:
    # your Cameras should be same as the dataset trained policy is on.
      laptop: laptop_cam/image
      front: front_cam/image
      robot_state: robot/pose
    outputs:
      - robot_action
      - status
    env:
      # Required settings
      MODEL_PATH: "/path/to/your/lerobot/model"
      TASK_DESCRIPTION: "pick up the cup"
      INFERENCE_FPS: "30"
      
      # Camera configuration
      CAMERA_NAMES: "laptop,front"
      CAMERA_LAPTOP_RESOLUTION: "480,640,3"
      CAMERA_FRONT_RESOLUTION: "480,640,3"      


  # Robot controller
  - id: robot_controller
    path: your-robot-controller
    inputs:
      action: policy_inference/robot_action # predicted joint state(rad)
    outputs:
      - pose

3. Start Policy Inference

dora build policy_inference.yml
dora run policy_inference.yml

The node will process camera inputs and robot state to generate actions for robot control.

Configuration

Required Environment Variables

Variable	Description	Example
MODEL_PATH	Path to trained LeRobot policy model directory	"outputs/train/your_policy/checkpoints/last/pretrained_model"
CAMERA_NAMES	Comma-separated camera names	"laptop,front,top"
CAMERA_*_RESOLUTION	Resolution for each camera (height,width,channels)	"480,640,3"
INFERENCE_FPS	Inference frequency	"30"
TASK_DESCRIPTION	Task description for task-conditioned policies	"Grab the red cube and and drop in the box."

Camera Configuration

For each camera defined in CAMERA_NAMES, you must set the resolution:

export CAMERA_NAMES="laptop,front,top"
export CAMERA_LAPTOP_RESOLUTION="1080,1920,3"
export CAMERA_FRONT_RESOLUTION="480,640,3" 
export CAMERA_TOP_RESOLUTION="480,640,3"

Model Requirements

The node expects LeRobot trained models with:

• A config.json file in the model directory
• Model weights compatible with LeRobot's from_pretrained() method

Device Selection

The node automatically selects the best available device:

• CUDA GPU (if available)
• MPS (Apple Silicon)
• CPU (fallback)

Device selection is handled by LeRobot's get_safe_torch_device() function.

See the examples/lerobot-dataset/policy_inference.yml for complete dataflow configurations.

License

This project is released under the MIT License.