random_agent.py

# Original Code:
# Copyright (c) 2022-2024, The Isaac Lab Project Developers.
# All rights reserved.
#
# Modifications:
# Copyright (c) 2024, Irvin Hwang
# SPDX-License-Identifier: BSD-3-Clause

"""Script to an environment with random action agent."""

"""Launch Isaac Sim Simulator first."""

import argparse

from omni.isaac.lab.app import AppLauncher

# add argparse arguments
parser = argparse.ArgumentParser(description="Random agent for Isaac Lab environments.")
parser.add_argument("--cpu", action="store_true", default=False, help="Use CPU pipeline.")
parser.add_argument(
    "--disable_fabric", action="store_true", default=False, help="Disable fabric and use USD I/O operations."
)
parser.add_argument("--num_envs", type=int, default=None, help="Number of environments to simulate.")
parser.add_argument("--task", type=str, default=None, help="Name of the task.")
# append AppLauncher cli args
AppLauncher.add_app_launcher_args(parser)
# parse the arguments
args_cli = parser.parse_args()

# launch omniverse app
app_launcher = AppLauncher(args_cli)
simulation_app = app_launcher.app

"""Rest everything follows."""

import gymnasium as gym
import torch


import jetbot 

from omni.isaac.lab_tasks.utils import parse_env_cfg


def main():
    """Random actions agent with Isaac Lab environment."""
    # create environment configuration
    env_cfg = parse_env_cfg(
        args_cli.task, use_gpu=not args_cli.cpu, num_envs=args_cli.num_envs, use_fabric=not args_cli.disable_fabric
    )
    # create environment
    env = gym.make(args_cli.task, cfg=env_cfg)

    # print info (this is vectorized environment)
    print(f"[INFO]: Gym observation space: {env.observation_space}")
    print(f"[INFO]: Gym action space: {env.action_space}")
    # reset environment
    env.reset()
    # simulate environment
    while simulation_app.is_running():
        # run everything in inference mode
        with torch.inference_mode():
            # sample actions from -1 to 1
            actions = (2 * torch.rand(env.action_space.shape, device=env.unwrapped.device) - 1) * -1
            # actions = torch.tensor([[0,0]], device='cuda:0')
            # apply actions
            env.step(actions)

    # close the simulator
    env.close()


if __name__ == "__main__":
    # run the main function
    main()
    # close sim app
    simulation_app.close()