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omniverse_sim.py
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omniverse_sim.py
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"""Script to play a checkpoint if an RL agent from RSL-RL."""
from __future__ import annotations
"""Launch Isaac Sim Simulator first."""
import argparse
from omni.isaac.lab.app import AppLauncher
import cli_args
import time
import os
import threading
# add argparse arguments
parser = argparse.ArgumentParser(description="Train an RL agent with RSL-RL.")
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=1, help="Number of environments to simulate.")
parser.add_argument("--task", type=str, default="Isaac-Velocity-Rough-Unitree-Go2-v0", help="Name of the task.")
parser.add_argument("--seed", type=int, default=None, help="Seed used for the environment")
parser.add_argument("--custom_env", type=str, default="office", help="Setup the environment")
parser.add_argument("--robot", type=str, default="go2", help="Setup the robot")
parser.add_argument("--robot_amount", type=int, default=1, help="Setup the robot amount")
# append RSL-RL cli arguments
cli_args.add_rsl_rl_args(parser)
# append AppLauncher cli args
AppLauncher.add_app_launcher_args(parser)
args_cli = parser.parse_args()
# launch omniverse app
app_launcher = AppLauncher(args_cli)
simulation_app = app_launcher.app
import omni
ext_manager = omni.kit.app.get_app().get_extension_manager()
ext_manager.set_extension_enabled_immediate("omni.isaac.ros2_bridge", True)
# FOR VR SUPPORT
# ext_manager.set_extension_enabled_immediate("omni.kit.xr.core", True)
# ext_manager.set_extension_enabled_immediate("omni.kit.xr.system.steamvr", True)
# ext_manager.set_extension_enabled_immediate("omni.kit.xr.system.simulatedxr", True)
# ext_manager.set_extension_enabled_immediate("omni.kit.xr.system.openxr", True)
# ext_manager.set_extension_enabled_immediate("omni.kit.xr.telemetry", True)
# ext_manager.set_extension_enabled_immediate("omni.kit.xr.profile.vr", True)
"""Rest everything follows."""
import gymnasium as gym
import torch
import carb
from omni.isaac.lab_tasks.utils import get_checkpoint_path
from omni.isaac.lab_tasks.utils.wrappers.rsl_rl import (
RslRlOnPolicyRunnerCfg,
RslRlVecEnvWrapper
)
import omni.isaac.lab.sim as sim_utils
import omni.appwindow
from rsl_rl.runners import OnPolicyRunner
import rclpy
from ros2 import RobotBaseNode, add_camera, add_rtx_lidar, pub_robo_data_ros2
from geometry_msgs.msg import Twist
from agent_cfg import unitree_go2_agent_cfg, unitree_g1_agent_cfg
from custom_rl_env import UnitreeGo2CustomEnvCfg, G1RoughEnvCfg
import custom_rl_env
from omnigraph import create_front_cam_omnigraph
def sub_keyboard_event(event, *args, **kwargs) -> bool:
if len(custom_rl_env.base_command) > 0:
if event.type == carb.input.KeyboardEventType.KEY_PRESS:
if event.input.name == 'W':
custom_rl_env.base_command[0] = [1, 0, 0]
if event.input.name == 'S':
custom_rl_env.base_command[0] = [-1, 0, 0]
if event.input.name == 'A':
custom_rl_env.base_command[0] = [0, 1, 0]
if event.input.name == 'D':
custom_rl_env.base_command[0] = [0, -1, 0]
if event.input.name == 'Q':
custom_rl_env.base_command[0] = [0, 0, 1]
if event.input.name == 'E':
custom_rl_env.base_command[0] = [0, 0, -1]
if len(custom_rl_env.base_command) > 1:
if event.input.name == 'I':
custom_rl_env.base_command[1] = [1, 0, 0]
if event.input.name == 'K':
custom_rl_env.base_command[1] = [-1, 0, 0]
if event.input.name == 'J':
custom_rl_env.base_command[1] = [0, 1, 0]
if event.input.name == 'L':
custom_rl_env.base_command[1] = [0, -1, 0]
if event.input.name == 'U':
custom_rl_env.base_command[1] = [0, 0, 1]
if event.input.name == 'O':
custom_rl_env.base_command[1] = [0, 0, -1]
elif event.type == carb.input.KeyboardEventType.KEY_RELEASE:
for i in range(len(custom_rl_env.base_command)):
custom_rl_env.base_command[i] = [0, 0, 0]
return True
def setup_custom_env():
try:
if (args_cli.custom_env == "warehouse"):
cfg_scene = sim_utils.UsdFileCfg(usd_path="./envs/warehouse.usd")
cfg_scene.func("/World/warehouse", cfg_scene, translation=(0.0, 0.0, 0.0))
if (args_cli.custom_env == "office"):
cfg_scene = sim_utils.UsdFileCfg(usd_path="./envs/office.usd")
cfg_scene.func("/World/office", cfg_scene, translation=(0.0, 0.0, 0.0))
except:
print("Error loading custom environment. You should download custom envs folder from: https://drive.google.com/drive/folders/1vVGuO1KIX1K6mD6mBHDZGm9nk2vaRyj3?usp=sharing")
def cmd_vel_cb(msg, num_robot):
x = msg.linear.x
y = msg.linear.y
z = msg.angular.z
custom_rl_env.base_command[num_robot] = [x, y, z]
def add_cmd_sub(num_envs):
node_test = rclpy.create_node('position_velocity_publisher')
for i in range(num_envs):
node_test.create_subscription(Twist, f'robot{i}/cmd_vel', lambda msg: cmd_vel_cb(msg, i), 10)
# Spin in a separate thread
thread = threading.Thread(target=rclpy.spin, args=(node_test, ), daemon=True)
thread.start()
def specify_cmd_for_robots(numv_envs):
base_cmd = []
for _ in range(numv_envs):
base_cmd.append([0, 0, 0])
custom_rl_env.base_command = base_cmd
def run_sim():
# acquire input interface
_input = carb.input.acquire_input_interface()
_appwindow = omni.appwindow.get_default_app_window()
_keyboard = _appwindow.get_keyboard()
_sub_keyboard = _input.subscribe_to_keyboard_events(_keyboard, sub_keyboard_event)
"""Play with RSL-RL agent."""
# parse configuration
env_cfg = UnitreeGo2CustomEnvCfg()
if args_cli.robot == "g1":
env_cfg = G1RoughEnvCfg()
# add N robots to env
env_cfg.scene.num_envs = args_cli.robot_amount
# create ros2 camera stream omnigraph
for i in range(env_cfg.scene.num_envs):
create_front_cam_omnigraph(i)
specify_cmd_for_robots(env_cfg.scene.num_envs)
agent_cfg: RslRlOnPolicyRunnerCfg = unitree_go2_agent_cfg
if args_cli.robot == "g1":
agent_cfg: RslRlOnPolicyRunnerCfg = unitree_g1_agent_cfg
# create isaac environment
env = gym.make(args_cli.task, cfg=env_cfg)
# wrap around environment for rsl-rl
env = RslRlVecEnvWrapper(env)
# specify directory for logging experiments
log_root_path = os.path.join("logs", "rsl_rl", agent_cfg["experiment_name"])
log_root_path = os.path.abspath(log_root_path)
print(f"[INFO] Loading experiment from directory: {log_root_path}")
resume_path = get_checkpoint_path(log_root_path, agent_cfg["load_run"], agent_cfg["load_checkpoint"])
print(f"[INFO]: Loading model checkpoint from: {resume_path}")
# load previously trained model
ppo_runner = OnPolicyRunner(env, agent_cfg, log_dir=None, device=agent_cfg["device"])
ppo_runner.load(resume_path)
print(f"[INFO]: Loading model checkpoint from: {resume_path}")
# obtain the trained policy for inference
policy = ppo_runner.get_inference_policy(device=env.unwrapped.device)
# reset environment
obs, _ = env.get_observations()
# initialize ROS2 node
rclpy.init()
base_node = RobotBaseNode(env_cfg.scene.num_envs)
add_cmd_sub(env_cfg.scene.num_envs)
annotator_lst = add_rtx_lidar(env_cfg.scene.num_envs, args_cli.robot, False)
add_camera(env_cfg.scene.num_envs, args_cli.robot)
setup_custom_env()
start_time = time.time()
# simulate environment
while simulation_app.is_running():
# run everything in inference mode
with torch.inference_mode():
# agent stepping
actions = policy(obs)
# env stepping
obs, _, _, _ = env.step(actions)
pub_robo_data_ros2(args_cli.robot, env_cfg.scene.num_envs, base_node, env, annotator_lst, start_time)
env.close()