القائمة
يُحدَّد Prompt الذي يراجع المصدر أولًا بشكل افتراضي. يمكنك التبديل إلى أمر مباشر أو تنزيل نسخة محلية.
اقرأ SKILL.md وأي ملفات مرافقة يعرضها SkillsMP قبل أن تقرر التثبيت.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
يتجاوز الأمر المباشر Prompt المخصّص للمراجعة. افحص المصدر قبل تشغيله.
npx skills add https://github.com/MIUAV/vibe-coding-ros2 --skill isaaclabيبقى الأمر في سطر واحد. مرّر أفقيًا لمراجعته كاملًا قبل النسخ.
تفضّل نسخة محلية؟ نزّل الملفات المتاحة حاليًا لدى SkillsMP.
استنادا إلى تصنيف SOC المهني
عرض SKILL.md
| name | isaaclab |
| description | NVIDIA Isaac Lab 仿真开发技能 - 强化学习训练、GPU 加速仿真、机器人控制 |
| argument-hint | isaaclab仿真 OR 强化学习训练 OR GPU仿真 OR 机器人学习 |
| user-invocable | true |
用于 Isaac Lab 仿真环境的配置和机器人训练
当需要以下帮助时使用此技能：
# 1. 安装 Isaac Sim (需要先安装 Omniverse)
# 下载 NVIDIA Isaac Sim 并解压
# 2. 克隆 Isaac Lab
cd ~/isaac-lab
git clone https://github.com/isaac-sim/IsaacLab.git
cd IsaacLab
# 3. 创建软链接到 Isaac Sim
ln -s /path/to/isaac-sim IsaacLab
# 4. 安装依赖
./scripts/setup.sh
# 5. 安装 Isaac Lab
pip install -e .
# 激活 conda 环境
conda activate isaaclab
# 运行示例环境
python scripts/run.py --task IsaacReachGoal-v0
# 启动 GPU 加速环境
python scripts/run.py --task IsaacReachGoal-v0 --gpu
IsaacLab/
├── isaaclab/ # 核心框架
│ ├── assets/ # 机器人资产
│ ├── envs/ # 环境实现
│ ├── sensors/ # 传感器
│ ├── utils/ # 工具函数
│ └── sim/ # 仿真器接口
├── scripts/ # 运行脚本
├── tools/ # 开发工具
├── docs/ # 文档
└── configs/ # 默认配置
# configs/my_env.py
from dataclass import dataclass
from isaaclab.envs import ManagerBasedRLEnvCfg
@dataclass
class MyEnvCfg:
# 奖励配置
reward_scales = {
"distance": -1.0,
"velocity": -0.1,
"action": -0.01,
}
# 奖励阈值
reward_thresholds = {
"success": 1.0,
}
# 终止条件
termination = {
"time_out": 1000,
"position_limit": 10.0,
}
# 动作空间
action_scale = 0.5
# 观察空间
num_obs = 20
num_actions = 8
# isaaclab_envs/my_env/my_env.py
import torch
from isaaclab.envs import ManagerBasedRLEnv
class MyRobotEnv(ManagerBasedRLEnv):
def __init__(self, cfg, sim_params, physics_engine, device, headless):
super().__init__(cfg, sim_params, physics_engine, device, headless)
def _reset_idx(self, env_ids: torch.Tensor):
"""重置指定环境"""
# 重置位置
self._robot.write_data_to_sim(env_ids)
# 重置缓冲区
self.reset_buf[env_ids] = 0
self.extras[env_ids] = {}
def _compute_reward(self, actions: torch.Tensor) -> torch.Tensor:
"""计算奖励"""
# 距离奖励
dist = torch.norm(self._robot.data.root_pos_w - self._goal, dim=-1)
dist_reward = -dist * self.cfg.reward_scales["distance"]
# 速度奖励
vel_reward = -torch.sum(
self._robot.data.root_lin_vel_w ** 2 +
self._robot.data.root_ang_vel_w ** 2,
dim=-1
) * self.cfg.reward_scales["velocity"]
# 动作惩罚
action_penalty = -torch.(actions ** , dim=-) * .cfg.reward_scales[]
dist_reward + vel_reward + action_penalty
() -> torch.Tensor:
time_out = .episode_length_buf >= .max_episode_length_s / .dt
out_of_bounds = torch.(
torch.(._robot.data.root_pos_w) > .cfg.termination[],
dim=-
)
time_out | out_of_bounds
# isaaclab/envs/__init__.py
from isaaclab.envs import (
ManagerBasedRLEnv,
differential_drive_flat,
)
from isaaclab.utils import register_class
register_class("MY_ENV", "my_env", "MyEnvCfg", my_env_cfg)
register_class("MY_ENV", "MyRobotEnv", "my_env", MyRobotEnv)
# configs/train.py
from isaaclab.envs import manager_based_rl_cfg
# RL 训练器配置
TRAIN_CFG = {
"algorithm": "PPO",
"num_steps_per_env": 24,
"learning_rate": 1e-3,
"num_learning_epochs": 5,
"num_mini_batches": 10,
"clip_param": 0.2,
"gamma": 0.99,
"lam": 0.95,
"value_loss_coef": 1.0,
"entropy_coef": 0.01,
"num_transitions_per_env": 128,
"learning_rate_schedule": "adaptive",
"schedule_decay": 0.5,
}
# 环境配置
ENV_CFG = manager_based_rl_cfg.ManagerBasedRLEnvCfg(
env_spacing=2.5,
episode_length_s=10,
)
# scripts/train.py
import torch
from isaaclab.algos import PPO
from isaaclab.envs import make_env
def train():
# 创建环境
env = make_env("IsaacReachGoal-v0", num_envs=256)
# 创建 PPO 智能体
ppo = PPO(
actor_lr=1e-3,
critic_lr=1e-3,
num_steps_per_env=24,
num_learning_epochs=5,
num_mini_batches=32,
clip_param=0.2,
gamma=0.99,
lam=0.95,
value_loss_coef=1.0,
entropy_coef=0.01,
)
# 训练循环
for i in range(1000):
# 收集经验
ppo.collect_rollout(env)
# 更新策略
ppo.update()
# 日志记录
if i % 10 == 0:
print(f"Step {i}, Reward: {env.reward_buf.mean():.4f}")
# 保存模型
ppo.save("policy.pt")
if __name__ == "__main__":
train()
from isaaclab.sensors import Camera
# 创建摄像头
camera = Camera(
name="front_camera",
width=640,
height=480,
horizontal_fov=90.0,
update_period=0.1,
)
robot.add_sensor("front_camera", camera)
# 获取图像
def _compute_obs(self):
camera_data = self._cameras.data.image["front_camera"]
return camera_data
# 深度摄像头配置
camera = Camera(
name="depth_camera",
width=640,
height=480,
colorize=False,
semantic_type=None,
)
# 启用深度
camera.set_mode("depth")
from isaaclab.sensors import RayCaster
# 激光雷达
ray_caster = RayCaster(
name="lidar",
sensor_type="lidar",
max_distance=30.0,
num_rays=1024,
)
robot.add_sensor("lidar", ray_caster)
from isaaclab.assets import Robot
# 加载机器人
robot = Robot(
name="manipulator",
file_path="assets/urdf/robot.urdf",
fix_base=True,
collision_group="default",
)
# 设置初始位置
robot.write_data_to_sim()
from isaaclab.assets import FlexBody
# 加载 MJCF
robot = FlexBody(
name="humanoid",
file_path="assets/mjcf/humanoid.xml",
mass=10.0,
friction=1.0,
)
# sim_params 配置
physx_params = {
"worker_thread_count": 4,
"solver_type": 1, # TGS
"use_gpu": True,
"num_position_iterations": 4,
"num_velocity_iterations": 0,
"contact_offset": 0.02,
"rest_offset": 0.001,
"bounce_threshold_velocity": 0.2,
"friction_offset_threshold": 0.04,
"friction_correlation_distance": 0.025,
"enable_sleeping": True,
"enable_stabilization": True,
}
sim_params = {
"device": "cuda:0",
"enable_cuda_raycast": True,
"physx": physx_params,
}
# 分布式训练配置
torch.distributed.init_process_group(
backend="nccl",
init_method="env://",
world_size=8,
rank=local_rank,
)
# 多个环境
env = make_env(
"IsaacReachGoal-v0",
num_envs=2048,
num_leaves_per_env=2,
)
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