工信部门备案网站,哪些网站可以做百科来源,沈阳做网站客户多吗,安徽网站设计找哪家目录 一、前言二、电脑配置三、配置步骤3.1 创建Conda环境3.2 安装PyTorch3.3 安装Isaac Sim3.4 安装Isaac Lab 四、总结 一、前言
博主自从去年开始就一直在关注Isaac Lab和Isaac Sim#xff0c;但是一直以来由于手头设备只有4060#xff0c;甚至没有达到最低配置16GB显存要… 目录 一、前言二、电脑配置三、配置步骤3.1 创建Conda环境3.2 安装PyTorch3.3 安装Isaac Sim3.4 安装Isaac Lab 四、总结 一、前言
博主自从去年开始就一直在关注Isaac Lab和Isaac Sim但是一直以来由于手头设备只有4060甚至没有达到最低配置16GB显存要求因此只能望洋兴叹。今年下定决心下血本购入顶配台式一台为了让我投入的资金充分转化为生产力因此最近开始捣鼓配置Isaac Lab和Isaac Sim。由于50系显卡较新且最新的CUDA版本只能使用Nightly版本的PyTorch因此配置过程中有许多需要注意的细节因此我写下了这篇博客用来记录配置过程既是记录配置过程中遇到的一些问题也是给各位志同道合的朋友们抛砖引玉一起用上最先进的GPU并行强化学习环境共同进步。话不多说我们正式开始配置吧。
二、电脑配置
名称型号操作系统Ubuntu 24.04 LTSCPUAMD Ryzen 9 9950X3D 16-Core Processor运行内存64GBGPUNVIDIA GeForce RTX 5090GPU 驱动575.64.03CUDA 版本12.9
三、配置步骤
电脑需要首先安装Nvidia驱动以及miniconda已有博客详细阐述了配置过程本文不再赘述。下面的配置过程主要参考Isaac Lab官方文档的Pip Installation文档提供了另外一种二进制安装方式主要区别在于使用的python环境以及Isaac Sim的安装上下面的安装步骤仅针对Pip Installation二进制安装请自行尝试。
3.1 创建Conda环境
conda create -n env_isaaclab python3.10
conda activate env_isaaclab3.2 安装PyTorch
此处区别于官方文档截至Sat Jul 26 14:55:10 2025 最新的CUDA版本12.9不能使用PyTorch的稳定版2.7.1因此需要安装Preview (Nightly)版本安装命令如下
pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu129使用如下命令查看当前的PyTorch版本
python -m pip show torch 2/dev/null | grep Version | awk {print $2}我的版本是Version: 2.9.0.dev20250725cu129需要记住这个版本号在下面安装Isaac Lab的时候要用到。
3.3 安装Isaac Sim
pip install isaacsim[all,extscache]4.5.0 --extra-index-url https://pypi.nvidia.com安装完成后使用如下命令验证Isaac Sim是否安装成功
isaacsim首次运行isaacsim时会有如下的NVIDIA Software License Agreement需要手动输入Yes。
By installing or using Isaac Sim, I agree to the terms of NVIDIA SOFTWARE LICENSE AGREEMENT (EULA)
in https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-software-license-agreementDo you accept the EULA? (Yes/No): Yes如果安装成功应该有如下界面
3.4 安装Isaac Lab
首先克隆仓库
git clone gitgithub.com:isaac-sim/IsaacLab.git安装依赖项
sudo apt install cmake build-essential使用编辑器修改IsaacLab/isaaclab.sh这一步使用到的版本号就是3.2节中最后我们获得的版本号中间用echo命令打印出来的信息可以不修改最关键的两点修改
if [[ ${torch_version} ! 2.7.0cu128 ]]; then 修改为 if [[ ${torch_version} ! 2.9.0.dev20250725cu129 ]]; then${python_exe} -m pip install torch2.7.0 torchvision0.22.0 --index-url https://download.pytorch.org/whl/cu128 修改为 ${python_exe} -m pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu129。
原本的安装脚本中会检查torch版本是否是 2.7.0cu128但是最新的CUDA版本是12.9支持12.9的只有Nightly版本因此需要把版本检查和安装的部分替换为最新的版本。
# pass the arguments
while [[ $# -gt 0 ]]; do# read the keycase $1 in-i|--install)# 把原先这一段注释修改成下面的echo [INFO] Installing extensions inside the Isaac Lab repository...python_exe$(extract_python_exe)# check if pytorch is installed and its version# install pytorch with cuda 12.9 for blackwell supportif ${python_exe} -m pip list 2/dev/null | grep -q torch; thentorch_version$(${python_exe} -m pip show torch 2/dev/null | grep Version: | awk {print $2})echo [INFO] Found PyTorch version ${torch_version} installed.if [[ ${torch_version} ! 2.9.0.dev20250725cu129 ]]; then # 替换此处的版本号echo [INFO] Uninstalling PyTorch version ${torch_version}...${python_exe} -m pip uninstall -y torch torchvision torchaudioecho [INFO] Installing PyTorch 2.9.0 with CUDA 12.9 support...${python_exe} -m pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu129elseecho [INFO] PyTorch 2.9.0 is already installed.fielseecho [INFO] Installing PyTorch 2.9.0 with CUDA 12.9 support...${python_exe} -m pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu129fi安装强化学习/模仿学习框架
./isaaclab.sh -i创建空场景验证安装
python scripts/tutorials/00_sim/create_empty.py应当能看到如下输出 下面我们就可以训练一个机器人了例如经典的ant环境
./isaaclab.sh -p scripts/reinforcement_learning/rsl_rl/train.py --taskIsaac-Ant-v0
# 如果要提高训练效率请加上--headless选项完整命令如下
# ./isaaclab.sh -p scripts/reinforcement_learning/rsl_rl/train.py --taskIsaac-Ant-v0 --headless机器人仿真2-视频训练ANT环境终端输出如下
################################################################################Learning iteration 205/1000 Computation: 82994 steps/s (collection: 1.520s, learning 0.059s)Mean action noise std: 0.16Mean value_function loss: 0.0247Mean surrogate loss: -0.0023Mean entropy loss: -3.9463Mean reward: 102.77Mean episode length: 906.04Episode_Reward/progress: 6.4252Episode_Reward/alive: 0.4708Episode_Reward/upright: 0.0927Episode_Reward/move_to_target: 0.4684Episode_Reward/action_l2: -0.0141Episode_Reward/energy: -0.7186Episode_Reward/joint_pos_limits: -0.3366Episode_Termination/time_out: 2.2812Episode_Termination/torso_height: 0.1562
--------------------------------------------------------------------------------Total timesteps: 27000832Iteration time: 1.58sTime elapsed: 00:04:59ETA: 00:19:15################################################################################Learning iteration 206/1000 Computation: 82106 steps/s (collection: 1.537s, learning 0.059s)Mean action noise std: 0.15Mean value_function loss: 0.0313Mean surrogate loss: -0.0002Mean entropy loss: -3.9886Mean reward: 105.27Mean episode length: 929.74Episode_Reward/progress: 6.6413Episode_Reward/alive: 0.4845Episode_Reward/upright: 0.0961Episode_Reward/move_to_target: 0.4757Episode_Reward/action_l2: -0.0146Episode_Reward/energy: -0.7420Episode_Reward/joint_pos_limits: -0.3487Episode_Termination/time_out: 2.3438Episode_Termination/torso_height: 0.1250
--------------------------------------------------------------------------------Total timesteps: 27131904Iteration time: 1.60sTime elapsed: 00:05:01ETA: 00:19:14
Isaac Lab中还有其他环境我们还可以训练机器人、机械臂、无人机等对象完成不同的任务例如我们可以训练Animal四足机器人
./isaaclab.sh -p scripts/reinforcement_learning/rsl_rl/train.py --taskIsaac-Velocity-Rough-Anymal-C-v0 # 同理可以加上--headless提高效率机器人仿真2-视频训练Anymal环境输出如下
################################################################################Learning iteration 113/1500 Computation: 24455 steps/s (collection: 3.956s, learning 0.064s)Mean action noise std: 0.49Mean value_function loss: 0.0140Mean surrogate loss: -0.0094Mean entropy loss: 8.4871Mean reward: 6.06Mean episode length: 515.96
Episode_Reward/track_lin_vel_xy_exp: 0.3945
Episode_Reward/track_ang_vel_z_exp: 0.2131Episode_Reward/lin_vel_z_l2: -0.0302Episode_Reward/ang_vel_xy_l2: -0.0485Episode_Reward/dof_torques_l2: -0.0438Episode_Reward/dof_acc_l2: -0.0953Episode_Reward/action_rate_l2: -0.0474Episode_Reward/feet_air_time: -0.0075Episode_Reward/undesired_contacts: -0.0016
Episode_Reward/flat_orientation_l2: 0.0000Episode_Reward/dof_pos_limits: 0.0000Curriculum/terrain_levels: 0.2560
Metrics/base_velocity/error_vel_xy: 0.2929
Metrics/base_velocity/error_vel_yaw: 0.2349Episode_Termination/time_out: 1.7917Episode_Termination/base_contact: 5.0417
--------------------------------------------------------------------------------Total timesteps: 11206656Iteration time: 4.02sTime elapsed: 00:06:54ETA: 01:24:00################################################################################Learning iteration 114/1500 Computation: 24878 steps/s (collection: 3.888s, learning 0.064s)Mean action noise std: 0.49Mean value_function loss: 0.0144Mean surrogate loss: -0.0091Mean entropy loss: 8.4738Mean reward: 6.77Mean episode length: 546.42
Episode_Reward/track_lin_vel_xy_exp: 0.3991
Episode_Reward/track_ang_vel_z_exp: 0.2165Episode_Reward/lin_vel_z_l2: -0.0308Episode_Reward/ang_vel_xy_l2: -0.0495Episode_Reward/dof_torques_l2: -0.0449Episode_Reward/dof_acc_l2: -0.0971Episode_Reward/action_rate_l2: -0.0484Episode_Reward/feet_air_time: -0.0080Episode_Reward/undesired_contacts: -0.0019
Episode_Reward/flat_orientation_l2: 0.0000Episode_Reward/dof_pos_limits: 0.0000Curriculum/terrain_levels: 0.2656
Metrics/base_velocity/error_vel_xy: 0.3042
Metrics/base_velocity/error_vel_yaw: 0.2438Episode_Termination/time_out: 2.0417Episode_Termination/base_contact: 5.3750
--------------------------------------------------------------------------------Total timesteps: 11304960Iteration time: 3.95sTime elapsed: 00:06:58ETA: 01:24:00
使用默认参数训练的CPU和内存占用情况如下 GPU占用情况如下不得不感叹5090的强大
(env_isaaclab) ➜ Environments nvidia-smi
Sat Jul 26 16:06:58 2025
-----------------------------------------------------------------------------------------
| NVIDIA-SMI 575.64.03 Driver Version: 575.64.03 CUDA Version: 12.9 |
|---------------------------------------------------------------------------------------
| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
||
| 0 NVIDIA GeForce RTX 5090 Off | 00000000:01:00.0 On | N/A |
| 0% 35C P0 171W / 575W | 17438MiB / 32607MiB | 25% Default |
| | | N/A |
--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
||
| 0 N/A N/A 2720 G /usr/lib/xorg/Xorg 613MiB |
| 0 N/A N/A 3027 G /usr/bin/gnome-shell 187MiB |
| 0 N/A N/A 3477 G ...exec/xdg-desktop-portal-gnome 8MiB |
| 0 N/A N/A 4017 G /usr/share/code/code 81MiB |
| 0 N/A N/A 4845 G ...ess --variations-seed-version 46MiB |
| 0 N/A N/A 5289 G ...ersion20250725-130039.589000 140MiB |
| 0 N/A N/A 6131 G ...OTP --variations-seed-version 63MiB |
| 0 N/A N/A 21308 CG .../envs/env_isaaclab/bin/python 15971MiB |
| 0 N/A N/A 21987 G /usr/bin/gnome-system-monitor 18MiB |
-----------------------------------------------------------------------------------------
四、总结
50系显卡虽强但软件生态还在逐步完善许多库尚未完全适配配置过程中需要频繁查阅 Nightly 版本、修补依赖、调整脚本。Ubuntu 24.04 虽然新但也存在一些兼容性问题比如 Isaac Sim 4.5 目前还不支持 ROS 2 Jazzy对于想要深度集成机器人中间件的用户来说需要提前规划。
接下来我还打算继续测试 Isaac Lab 在多智能体协同、规划与博弈任务中的表现并尝试集成 ROS 2 等模块构建更加完善的实验平台。希望这篇文章能为正在探索这条技术路线的朋友带来一些参考也欢迎大家留言交流、一起摸索进步。
