Train Vision-Language-Action Robot Policies in NVIDIA Isaac Sim with LeRobot

                      
                     
from typing import Dict, Any
import os
import numpy as np
from isaac import Robot, Simulator
from le_robot import LanguageModel, VisionModel

# Configuration
API_KEY = os.getenv('API_KEY')  # Securely manage API keys
SIMULATION_CONFIG = {'render': True, 'time_step': 0.02}

# Initialize models
language_model = LanguageModel(api_key=API_KEY)
vision_model = VisionModel()

# Initialize simulator
simulator = Simulator(config=SIMULATION_CONFIG)
robot = Robot(simulator=simulator)

# Core logic for training policies
async def train_robot_policy(params: Dict[str, Any]) -> Dict[str, Any]:
    try:
        for step in range(100):  # Training loop
            observation = await vision_model.capture_observation()
            action = language_model.generate_action(observation)
            await robot.perform_action(action)
            if step % 10 == 0:
                print(f"Step {step}: Executed action {action}")
        return {'success': True, 'message': 'Training complete'}
    except Exception as error:
        return {'success': False, 'error': str(error)}

if __name__ == '__main__':
    simulator.start()
    result = await train_robot_policy({})
    print(result)