File size: 3,002 Bytes
d6b3397 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 | # Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md).
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
"""Script to an environment with random action agent."""
import argparse
import contextlib
import sys
import gymnasium as gym
import torch
import isaaclab_tasks # noqa: F401
with contextlib.suppress(ImportError):
import isaaclab_tasks_experimental # noqa: F401
from isaaclab_tasks.utils import (
add_launcher_args,
launch_simulation,
resolve_task_config,
setup_preset_cli,
)
# add argparse arguments
parser = argparse.ArgumentParser(description="Random agent for Isaac Lab environments.")
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
add_launcher_args(parser)
# simple agents should open Kit visualizer by default
parser.set_defaults(visualizer=["kit"])
args_cli, hydra_args = setup_preset_cli(parser)
sys.argv = [sys.argv[0]] + hydra_args
# PLACEHOLDER: Extension template (do not remove this comment)
def main():
"""Random actions agent with Isaac Lab environment."""
torch.manual_seed(42)
# parse configuration via Hydra (supports preset selection, e.g. env.sim.physics=newton_mjwarp)
env_cfg, _ = resolve_task_config(args_cli.task, "")
with launch_simulation(env_cfg, args_cli):
# override with CLI arguments
env_cfg.scene.num_envs = args_cli.num_envs if args_cli.num_envs is not None else env_cfg.scene.num_envs
env_cfg.sim.device = args_cli.device if args_cli.device is not None else env_cfg.sim.device
if args_cli.disable_fabric:
env_cfg.sim.use_fabric = False
# 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
sim = env.unwrapped.sim
while True:
if sim.visualizers:
# visualizer mode: run until the visualizer window is closed
if not any(v.is_running() and not v.is_closed for v in sim.visualizers):
break
# 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
# apply actions
env.step(actions)
# close the simulator
env.close()
if __name__ == "__main__":
# run the main function
main()
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