# 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 the environment for a quadruped robot with height-scan sensor. In this example, we use a locomotion policy to control the robot. The robot is commanded to move forward at a constant velocity. The height-scan sensor is used to detect the height of the terrain. .. code-block:: bash # Run the script ./isaaclab.sh -p scripts/tutorials/03_envs/create_quadruped_base_env.py --num_envs 32 """ """Launch Isaac Sim Simulator first.""" import argparse from isaaclab.app import AppLauncher # add argparse arguments parser = argparse.ArgumentParser(description="Tutorial on creating a quadruped base environment.") parser.add_argument("--num_envs", type=int, default=64, help="Number of environments to spawn.") # 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 torch import isaaclab.envs.mdp as mdp import isaaclab.sim as sim_utils from isaaclab.assets import ArticulationCfg, AssetBaseCfg from isaaclab.envs import ManagerBasedEnv, ManagerBasedEnvCfg from isaaclab.managers import EventTermCfg as EventTerm from isaaclab.managers import ObservationGroupCfg as ObsGroup from isaaclab.managers import ObservationTermCfg as ObsTerm from isaaclab.managers import SceneEntityCfg from isaaclab.scene import InteractiveSceneCfg from isaaclab.sensors import RayCasterCfg, patterns from isaaclab.terrains import TerrainImporterCfg from isaaclab.utils import configclass from isaaclab.utils.assets import ISAACLAB_NUCLEUS_DIR, check_file_path, read_file from isaaclab.utils.noise import AdditiveUniformNoiseCfg as Unoise ## # Pre-defined configs ## from isaaclab.terrains.config.rough import ROUGH_TERRAINS_CFG # isort: skip from isaaclab_assets.robots.anymal import ANYMAL_C_CFG # isort: skip ## # Custom observation terms ## def constant_commands(env: ManagerBasedEnv) -> torch.Tensor: """The generated command from the command generator.""" return torch.tensor([[1, 0, 0]], device=env.device).repeat(env.num_envs, 1) ## # Scene definition ## @configclass class MySceneCfg(InteractiveSceneCfg): """Example scene configuration.""" # add terrain terrain = TerrainImporterCfg( prim_path="/World/ground", terrain_type="generator", terrain_generator=ROUGH_TERRAINS_CFG, max_init_terrain_level=5, collision_group=-1, physics_material=sim_utils.RigidBodyMaterialCfg( friction_combine_mode="multiply", restitution_combine_mode="multiply", static_friction=1.0, dynamic_friction=1.0, ), debug_vis=False, ) # add robot robot: ArticulationCfg = ANYMAL_C_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot") # sensors height_scanner = RayCasterCfg( prim_path="{ENV_REGEX_NS}/Robot/base", offset=RayCasterCfg.OffsetCfg(pos=(0.0, 0.0, 20.0)), ray_alignment="yaw", pattern_cfg=patterns.GridPatternCfg(resolution=0.1, size=[1.6, 1.0]), debug_vis=True, mesh_prim_paths=["/World/ground"], ) # lights light = AssetBaseCfg( prim_path="/World/light", spawn=sim_utils.DistantLightCfg(color=(0.75, 0.75, 0.75), intensity=3000.0), ) ## # MDP settings ## @configclass class ActionsCfg: """Action specifications for the MDP.""" joint_pos = mdp.JointPositionActionCfg(asset_name="robot", joint_names=[".*"], scale=0.5, use_default_offset=True) @configclass class ObservationsCfg: """Observation specifications for the MDP.""" @configclass class PolicyCfg(ObsGroup): """Observations for policy group.""" # observation terms (order preserved) base_lin_vel = ObsTerm(func=mdp.base_lin_vel, noise=Unoise(n_min=-0.1, n_max=0.1)) base_ang_vel = ObsTerm(func=mdp.base_ang_vel, noise=Unoise(n_min=-0.2, n_max=0.2)) projected_gravity = ObsTerm( func=mdp.projected_gravity, noise=Unoise(n_min=-0.05, n_max=0.05), ) velocity_commands = ObsTerm(func=constant_commands) joint_pos = ObsTerm(func=mdp.joint_pos_rel, noise=Unoise(n_min=-0.01, n_max=0.01)) joint_vel = ObsTerm(func=mdp.joint_vel_rel, noise=Unoise(n_min=-1.5, n_max=1.5)) actions = ObsTerm(func=mdp.last_action) height_scan = ObsTerm( func=mdp.height_scan, params={"sensor_cfg": SceneEntityCfg("height_scanner")}, noise=Unoise(n_min=-0.1, n_max=0.1), clip=(-1.0, 1.0), ) def __post_init__(self): self.enable_corruption = True self.concatenate_terms = True # observation groups policy: PolicyCfg = PolicyCfg() @configclass class EventCfg: """Configuration for events.""" reset_scene = EventTerm(func=mdp.reset_scene_to_default, mode="reset") ## # Environment configuration ## @configclass class QuadrupedEnvCfg(ManagerBasedEnvCfg): """Configuration for the locomotion velocity-tracking environment.""" # Scene settings scene: MySceneCfg = MySceneCfg(num_envs=args_cli.num_envs, env_spacing=2.5) # Basic settings observations: ObservationsCfg = ObservationsCfg() actions: ActionsCfg = ActionsCfg() events: EventCfg = EventCfg() def __post_init__(self): """Post initialization.""" # general settings self.decimation = 4 # env decimation -> 50 Hz control # simulation settings self.sim.dt = 0.005 # simulation timestep -> 200 Hz physics self.sim.physics_material = self.scene.terrain.physics_material self.sim.device = args_cli.device # update sensor update periods # we tick all the sensors based on the smallest update period (physics update period) if self.scene.height_scanner is not None: self.scene.height_scanner.update_period = self.decimation * self.sim.dt # 50 Hz def main(): """Main function.""" # setup base environment env_cfg = QuadrupedEnvCfg() env = ManagerBasedEnv(cfg=env_cfg) # load level policy policy_path = ISAACLAB_NUCLEUS_DIR + "/Policies/ANYmal-C/HeightScan/policy.pt" # check if policy file exists if not check_file_path(policy_path): raise FileNotFoundError(f"Policy file '{policy_path}' does not exist.") file_bytes = read_file(policy_path) # jit load the policy policy = torch.jit.load(file_bytes).to(env.device).eval() # simulate physics count = 0 obs, _ = env.reset() while simulation_app.is_running(): with torch.inference_mode(): # reset if count % 1000 == 0: obs, _ = env.reset() count = 0 print("-" * 80) print("[INFO]: Resetting environment...") # infer action action = policy(obs["policy"]) # step env obs, _ = env.step(action) # update counter count += 1 # close the environment env.close() if __name__ == "__main__": # run the main function main() # close sim app simulation_app.close()