Train Robotic Manipulation Policies with LeRobot and Isaac Lab

                      
                     
import os
import numpy as np
from typing import List, Dict
from isaac import Robot, Controller

# Configuration
class Config:
    MODEL_PATH: str = os.getenv('MODEL_PATH', './models/')
    NUM_EPISODES: int = int(os.getenv('NUM_EPISODES', 100))
    LEARNING_RATE: float = float(os.getenv('LEARNING_RATE', 0.001))

# Initialize the robot and controller
class RobotPolicy:
    def __init__(self, robot: Robot):
        self.robot = robot
        self.controller = Controller(robot)

    def train(self) -> None:
        for episode in range(Config.NUM_EPISODES):
            try:
                self.run_episode(episode)
            except Exception as e:
                print(f'Error during episode {episode}: {e}')

    def run_episode(self, episode: int) -> None:
        # Logic for running a single training episode
        observations = self.robot.get_observations()
        actions = self.policy(observations)
        rewards = self.controller.step(actions)
        self.update_model(rewards)

    def policy(self, observations: List[float]) -> List[float]:
        # Placeholder for policy logic
        return np.random.uniform(-1, 1, size=len(observations)).tolist()

    def update_model(self, rewards: float) -> None:
        # Logic to update the model with rewards
        pass

if __name__ == '__main__':
    robot = Robot()  # Initialize your robot
    robot_policy = RobotPolicy(robot)
    robot_policy.train()