cmvr_ai_lab/scripts/replay_npz.py

"""This script demonstrates how to use the interactive scene interface to setup a scene with multiple prims.

.. code-block:: bash

    # Usage
    python replay_motion.py --motion_file dataset/xxxx.npz
"""

"""Launch Isaac Sim Simulator first."""

import argparse
import os
import numpy as np
import torch
from typing import Sequence

from isaaclab.app import AppLauncher

# add argparse arguments
parser = argparse.ArgumentParser(description="Replay converted motions.")
parser.add_argument("--motion_file", type=str, required=True, help="Path to the motion file (.npz).")

# 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 isaaclab.sim as sim_utils
from isaaclab.assets import Articulation, ArticulationCfg, AssetBaseCfg
from isaaclab.scene import InteractiveScene, InteractiveSceneCfg
from isaaclab.sim import SimulationContext
from isaaclab.utils import configclass
from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR

# ! terrain
from isaaclab.terrains import TerrainImporterCfg


##
# Pre-defined configs
##
from engineai_lab.robots.pm01 import PM01_CFG

class MotionLoader:
    def __init__(self, motion_file: str, body_indexes: Sequence[int], device: str = "cpu"):
        assert os.path.isfile(motion_file), f"Invalid file path: {motion_file}"
        data = np.load(motion_file)

        self.fps = data["fps"]
        self.joint_pos = torch.tensor(data["joint_pos"], dtype=torch.float32, device=device)
        self.joint_vel = torch.tensor(data["joint_vel"], dtype=torch.float32, device=device)
        self._body_pos_w = torch.tensor(data["body_pos_w"], dtype=torch.float32, device=device)
        self._body_quat_w = torch.tensor(data["body_quat_w"], dtype=torch.float32, device=device)
        self._body_lin_vel_w = torch.tensor(data["body_lin_vel_w"], dtype=torch.float32, device=device)
        self._body_ang_vel_w = torch.tensor(data["body_ang_vel_w"], dtype=torch.float32, device=device)
        self._body_indexes = body_indexes
        self.time_step_total = self.joint_pos.shape[0]

    @property
    def body_pos_w(self) -> torch.Tensor:
        return self._body_pos_w[:, self._body_indexes]

    @property
    def body_quat_w(self) -> torch.Tensor:
        return self._body_quat_w[:, self._body_indexes]

    @property
    def body_lin_vel_w(self) -> torch.Tensor:
        return self._body_lin_vel_w[:, self._body_indexes]

    @property
    def body_ang_vel_w(self) -> torch.Tensor:
        return self._body_ang_vel_w[:, self._body_indexes]


@configclass
class ReplayMotionsSceneCfg(InteractiveSceneCfg):
    """Configuration for a replay motions scene."""

    terrain = TerrainImporterCfg(
        prim_path="/World/ground",
        terrain_type="plane",
        collision_group=-1,
        physics_material=sim_utils.RigidBodyMaterialCfg(
            friction_combine_mode="multiply",
            restitution_combine_mode="multiply",
            static_friction=1.0,
            dynamic_friction=1.0,
        ),
        visual_material=sim_utils.MdlFileCfg(
            mdl_path="{NVIDIA_NUCLEUS_DIR}/Materials/Base/Architecture/Shingles_01.mdl",
            project_uvw=True,
        ),
    )

    sky_light = AssetBaseCfg(
        prim_path="/World/skyLight",
        spawn=sim_utils.DomeLightCfg(
            intensity=750.0,
            texture_file=f"{ISAAC_NUCLEUS_DIR}/Materials/Textures/Skies/PolyHaven/kloofendal_43d_clear_puresky_4k.hdr",
        ),
    )

    # articulation
    robot: ArticulationCfg = PM01_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot")


def run_simulator(sim: sim_utils.SimulationContext, scene: InteractiveScene, motion_loader: MotionLoader):
    # Extract scene entities
    robot: Articulation = scene["robot"]
    # Define simulation stepping
    sim_dt = sim.get_physics_dt()

    motion = motion_loader
    time_steps = torch.zeros(scene.num_envs, dtype=torch.long, device=sim.device)

    # Simulation loop
    while simulation_app.is_running():
        time_steps += 1
        reset_ids = time_steps >= motion.time_step_total
        time_steps[reset_ids] = 0

        root_states = robot.data.default_root_state.clone()
        root_states[:, :3] = motion.body_pos_w[time_steps][:, 0] + scene.env_origins[:, None, :]
        root_states[:, 3:7] = motion.body_quat_w[time_steps][:, 0]
        root_states[:, 7:10] = motion.body_lin_vel_w[time_steps][:, 0]
        root_states[:, 10:] = motion.body_ang_vel_w[time_steps][:, 0]

        robot.write_root_state_to_sim(root_states)
        robot.write_joint_state_to_sim(motion.joint_pos[time_steps], motion.joint_vel[time_steps])
        scene.write_data_to_sim()
        sim.render()  # We don't want physic (sim.step())
        scene.update(sim_dt)

        pos_lookat = root_states[0, :3].cpu().numpy()
        sim.set_camera_view(pos_lookat + np.array([2.0, 2.0, 0.5]), pos_lookat)


def main():
    sim_cfg = sim_utils.SimulationCfg(device=args_cli.device)
    sim_cfg.dt = 0.01
    sim = SimulationContext(sim_cfg)

    motion_loader = MotionLoader(
        motion_file=args_cli.motion_file,
        body_indexes=torch.tensor([0], dtype=torch.long, device=sim.device),
        device=sim.device,
    )

    scene_cfg = ReplayMotionsSceneCfg(num_envs=1, env_spacing=2.0)

    scene = InteractiveScene(scene_cfg)

    sim.reset()
    # Run the simulator
    run_simulator(sim, scene, motion_loader)


if __name__ == "__main__":
    # run the main function
    main()
    # close sim app
    simulation_app.close()