File size: 2,624 Bytes
406662d | 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 88 89 | # 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
"""
This script demonstrates policy inference in a prebuilt USD environment.
In this example, we use a locomotion policy to control the H1 robot. The robot was trained
using Isaac-Velocity-Rough-H1-v0. The robot is commanded to move forward at a constant velocity.
.. code-block:: bash
# Run the script
./isaaclab.sh -p scripts/tutorials/03_envs/policy_inference_in_usd.py --checkpoint /path/to/jit/checkpoint.pt
"""
"""Launch Isaac Sim Simulator first."""
import argparse
from isaaclab.app import AppLauncher
# add argparse arguments
parser = argparse.ArgumentParser(description="Tutorial on inferencing a policy on an H1 robot in a warehouse.")
parser.add_argument("--checkpoint", type=str, help="Path to model checkpoint exported as jit.", required=True)
# 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 io
import os
import torch
import omni
from isaaclab.envs import ManagerBasedRLEnv
from isaaclab.terrains import TerrainImporterCfg
from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR
from isaaclab_tasks.manager_based.locomotion.velocity.config.h1.rough_env_cfg import H1RoughEnvCfg_PLAY
def main():
"""Main function."""
# load the trained jit policy
policy_path = os.path.abspath(args_cli.checkpoint)
file_content = omni.client.read_file(policy_path)[2]
file = io.BytesIO(memoryview(file_content).tobytes())
policy = torch.jit.load(file, map_location=args_cli.device)
# setup environment
env_cfg = H1RoughEnvCfg_PLAY()
env_cfg.scene.num_envs = 1
env_cfg.curriculum = None
env_cfg.scene.terrain = TerrainImporterCfg(
prim_path="/World/ground",
terrain_type="usd",
usd_path=f"{ISAAC_NUCLEUS_DIR}/Environments/Simple_Warehouse/warehouse.usd",
)
env_cfg.sim.device = args_cli.device
if args_cli.device == "cpu":
env_cfg.sim.use_fabric = False
# create environment
env = ManagerBasedRLEnv(cfg=env_cfg)
# run inference with the policy
obs, _ = env.reset()
with torch.inference_mode():
while simulation_app.is_running():
action = policy(obs["policy"])
obs, _, _, _, _ = env.step(action)
if __name__ == "__main__":
main()
simulation_app.close()
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