前言

之前的文章介绍了导入object并设置静态和动态的物理属性。这篇文章主要介绍articulation。控制articulation类其实底层思路和控制object差不多，导入物体类函数，设置物理参数，生成物体在仿真环境中，通过该类直接写入位置，速度或者驱动力。文章还是基于官方的tutorial结合自己的开发经验进行注解。官方文档链接：Interacting with an articulation — Isaac Lab Documentation

教程代码

# Copyright (c) 2022-2025, The Isaac Lab Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause

"""This script demonstrates how to spawn a cart-pole and interact with it.

.. code-block:: bash

    # Usage
    ./isaaclab.sh -p scripts/tutorials/01_assets/run_articulation.py

"""

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


import argparse

from isaaclab.app import AppLauncher

# add argparse arguments
parser = argparse.ArgumentParser(description="Tutorial on spawning and interacting with an articulation.")
# 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 isaacsim.core.utils.prims as prim_utils

import isaaclab.sim as sim_utils
from isaaclab.assets import Articulation
from isaaclab.sim import SimulationContext

##
# Pre-defined configs
## 

# 直接导入cartpole的配置文件类，然后用Articulation包装生成可交互的articulation在仿真环境
from isaaclab_assets import CARTPOLE_CFG  # isort:skip

# design_scene()用来创建仿真环境并导入需要的asset，这个环境是静态可交互的
def design_scene() -> tuple[dict, list[list[float]]]:
    """Designs the scene."""
    # Ground-plane
    cfg = sim_utils.GroundPlaneCfg()
    cfg.func("/World/defaultGroundPlane", cfg)
    # Lights
    cfg = sim_utils.DomeLightCfg(intensity=3000.0, color=(0.75, 0.75, 0.75))
    cfg.func("/World/Light", cfg)

    # Create separate groups called "Origin1", "Origin2"
    # Each group will have a robot in it
    origins = [[0.0, 0.0, 0.0], [-1.0, 0.0, 0.0]]
    # Origin 1
    prim_utils.create_prim("/World/Origin1", "Xform", translation=origins[0])
    # Origin 2
    prim_utils.create_prim("/World/Origin2", "Xform", translation=origins[1])

    # Articulation
    cartpole_cfg = CARTPOLE_CFG.copy()
    cartpole_cfg.prim_path = "/World/Origin.*/Robot"
    cartpole = Articulation(cfg=cartpole_cfg)

    # return the scene information
    scene_entities = {"cartpole": cartpole}
    return scene_entities, origins

# run_simulator()是开启仿真pipeline的，根据已经创建的静态仿真环境，控制机器人和物体、环境进行交互，并推理，渲染。
def run_simulator(sim: sim_utils.SimulationContext, entities: dict[str, Articulation], origins: torch.Tensor):
    """Runs the simulation loop."""
    # Extract scene entities
    # note: we only do this here for readability. In general, it is better to access the entities directly from
    #   the dictionary. This dictionary is replaced by the InteractiveScene class in the next tutorial.
    robot = entities["cartpole"]
    # Define simulation stepping
    sim_dt = sim.get_physics_dt()
    count = 0
    # Simulation loop
    while simulation_app.is_running():
        # Reset
        if count % 500 == 0:
            # reset counter
            count = 0
            # reset the scene entities
            # root state
            # we offset the root state by the origin since the states are written in simulation world frame
            # if this is not done, then the robots will be spawned at the (0, 0, 0) of the simulation world
            root_state = robot.data.default_root_state.clone()
            root_state[:, :3] += origins
            robot.write_root_pose_to_sim(root_state[:, :7])
            robot.write_root_velocity_to_sim(root_state[:, 7:])
            # set joint positions with some noise
            joint_pos, joint_vel = robot.data.default_joint_pos.clone(), robot.data.default_joint_vel.clone()
            joint_pos += torch.rand_like(joint_pos) * 0.1
            robot.write_joint_state_to_sim(joint_pos, joint_vel)
            # clear internal buffers
            robot.reset()
            print("[INFO]: Resetting robot state...")
        # Apply random action
        # -- generate random joint efforts
        efforts = torch.randn_like(robot.data.joint_pos) * 5.0
        # -- apply action to the robot
        robot.set_joint_effort_target(efforts)
        # -- write data to sim
        robot.write_data_to_sim()
        # Perform step
        sim.step()
        # Increment counter
        count += 1
        # Update buffers
        robot.update(sim_dt)

#整体思路是：创建可交互的仿真环境->控制机器人(effort,position,velocity三种方式)->物理仿真推理->渲染(如果开启GUI的话)

def main():
    """Main function."""
    # Load kit helper
    sim_cfg = sim_utils.SimulationCfg(device=args_cli.device)
    sim = SimulationContext(sim_cfg)
    # Set main camera
    sim.set_camera_view([2.5, 0.0, 4.0], [0.0, 0.0, 2.0])
    # Design scene
    scene_entities, scene_origins = design_scene()
    scene_origins = torch.tensor(scene_origins, device=sim.device)
    # Play the simulator
    sim.reset()
    # Now we are ready!
    print("[INFO]: Setup complete...")
    # Run the simulator
    run_simulator(sim, scene_entities, scene_origins)


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