87 lines
2.9 KiB
Python
87 lines
2.9 KiB
Python
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# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md).
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# All rights reserved.
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#
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# SPDX-License-Identifier: BSD-3-Clause
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"""Script to run an environment with zero action agent."""
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import argparse
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import contextlib
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import sys
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import gymnasium as gym
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import torch
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import isaaclab_tasks # noqa: F401
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with contextlib.suppress(ImportError):
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import isaaclab_tasks_experimental # noqa: F401
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from isaaclab_tasks.utils import (
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add_launcher_args,
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launch_simulation,
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resolve_task_config,
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setup_preset_cli,
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)
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# add argparse arguments
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parser = argparse.ArgumentParser(description="Zero agent for Isaac Lab environments.")
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parser.add_argument(
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"--disable_fabric", action="store_true", default=False, help="Disable fabric and use USD I/O operations."
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)
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parser.add_argument("--num_envs", type=int, default=None, help="Number of environments to simulate.")
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parser.add_argument("--task", type=str, default=None, help="Name of the task.")
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# append AppLauncher cli args
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add_launcher_args(parser)
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# simple agents should open Kit visualizer by default
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parser.set_defaults(visualizer=["kit"])
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args_cli, hydra_args = setup_preset_cli(parser)
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sys.argv = [sys.argv[0]] + hydra_args
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import go2Demo.tasks # noqa: F401
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MAX_STEPS = 100
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def main():
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"""Zero actions agent with Isaac Lab environment."""
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torch.manual_seed(42)
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# parse configuration via Hydra (supports preset selection, e.g. env.sim.physics=newton_mjwarp)
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env_cfg, _ = resolve_task_config(args_cli.task, "")
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with launch_simulation(env_cfg, args_cli):
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# override with CLI arguments
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env_cfg.scene.num_envs = args_cli.num_envs if args_cli.num_envs is not None else env_cfg.scene.num_envs
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env_cfg.sim.device = args_cli.device if args_cli.device is not None else env_cfg.sim.device
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if args_cli.disable_fabric:
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env_cfg.sim.use_fabric = False
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# create environment
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env = gym.make(args_cli.task, cfg=env_cfg)
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# print info (this is vectorized environment)
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print(f"[INFO]: Gym observation space: {env.observation_space}")
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print(f"[INFO]: Gym action space: {env.action_space}")
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# reset environment
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env.reset()
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# simulate environment
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# keep running while any visualizer is open, otherwise fall back to MAX_STEPS
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sim = env.unwrapped.sim
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actions = torch.zeros(env.action_space.shape, device=env.unwrapped.device)
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while True:
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if sim.visualizers:
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# visualizer mode: run until the visualizer window is closed
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if not any(v.is_running() and not v.is_closed for v in sim.visualizers):
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break
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# run everything in inference mode
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with torch.inference_mode():
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# apply actions
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env.step(actions)
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# close the simulator
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env.close()
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if __name__ == "__main__":
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# run the main function
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main()
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