英伟达isaac教程

大家好，我是 nvidia 的李雨倩莉莉。上一期视频我们介绍了 s x m 的下载和安装。在这期视频中，我们将从两个视力演示为大家介绍 s x m 中最常用的用户界面、按钮、菜单和控件。 在这里，我们先从 hello world 的视力演示开始，让大家更快速地了解 s x m 的操作界面、代码导入以及如何添加新的视力并转为独立应用程序。首先启动 s x m， 然后在菜单栏 essac examples 中点击 hollow world， 这时会弹出一个导入代码文件的弹窗。如果要把场景导入 录制 isaacsim， 可以点击 load。 如果要修改代码，可以点击右上角的编辑图标。点击 load 后，可以看到我们加载了一个空白场景。 如果这个时候想要点击 open source code， 会发现这个图标无法使用。这是因为还没有安装 visual studio code 原代码编辑器，所以在运行前请留意您是否已下载和安装原代码编辑器。 这时候点击 open source code， 发现可以正常运行并弹出原代码编辑器窗口，并可以进行代码编写。可以从官网中复制代码至原代 码编辑器中进行编写并保存。更多详细的代码教程可至 nvd 啊官网查询学习。 点击菜单栏 file， 新建一个空场景重新加载代码后，可以看到场景中多了一个小立方体。 到目前为止，您一直在编辑 hello word 视力。接下来您将在 izac 视力菜单下创建一个新视力， 具体过程请参考官网操作步骤。这时可以看到菜单栏 esoc examples 中多了一个选项， awesome example。 这就是我们刚才新建的一个事例。 将最终的代码复制到原代码编辑器中，保存并重新加载，然后就可以看到一个 完整的场景。 接下来将视力转换为独立应用程序，从而使机器人 a p p 在拍丧启动时立即启动。您可以控制核实进行物理渲染。 打开一个新的 my application， 拍上文件并添加代码并运行。 这里演示如何使用 jupiter notebook。 将视力转换为独立应用程序。最终通过启动器或终端启动 s x m 后打开同 同一个 usd 文件，然后打开实时同步选项，同时直接在 omivers sxim 故意中查看场景。接下来我们来体验另一个视力 rose two sxim 目前与 rose two 有很好的扩展兼容。首先可按照官网步骤构建 rose two 工作区。 要启用 rose to bring 扩展，请转到扩展管理器菜单 window extensions 并搜索 rose bridge。 在任何给定时间只能启用一个 rose bridge 扩展。在菜单栏中选择 izac examples rose navigation 开始渲染以加载仓库场景。 点击左侧工具栏 play 按键开始模拟。要使用此工作区中构建 rose two 包，请先打开一个新终端，并使用视频中的命令获取工作区 转到 isaac 视力。 rose 导航已加载仓库场景，点击 play 开始模拟。在相应的目录下运行这三条命令，并打开 rose to 导航。单击 navigation to go 按钮，然后单击并拖动地图中所需的位置点那位 to 现在将生成一条轨迹，机器人将开始向其目的地移动。 机器人在运动的画面中会时时反馈在 s x sim 界面。接下来的课程我们会带来更多的功能介绍，大家敬请期待。

it's not enough for humans to imagine， we have to invent and explore and push beyond what's been done we create smarter and faster， we push it to fail， so it can learn we teach it then help it teach itself we broaden its understanding to take on new challenges with absolute precision and succeed we make it perceive and move and even reason so it can share our world with us this is where inspiration leads us the next frontier this is a video of project group a general purpose foundation model for humanoid robot learning the group model takes multimodal instructions and past interactions as input and produces the next action for the robot to execute we developed isaac lab a robot learning application to train group on omniverse isaac sim and we scale out with osmo a new compute orchestration service that coordinates workflows across dgx systems for training and ovx systems for simulation with these tools we can train groot and physically based simulation and transfer zero shot to the real world the groot model will enable a robot to learn from a handful of human demonstrations， so it can help with everyday tasks an emulate human movement just by observing us this is made possible with nvidia's technologies that can understand humans from videos， train models and simulation and ultimately deploy them directly to physical robots connecting group to a large language model even allows it to generate motions by following natural language instructions hi， jail one can you give me a high five sure big？ let's high five can you give us some cool moves dirt check this out all this incredible intelligence is powered by the jetson thor robotics chips designed for grouped built for the future with isaac， lav， osmo and groot we're providing the building blocks for the next generation of ai powered robotics。

successful development training and testing of complex robots for real world applications demand high fidelity simulation and accurate physics built on nvidia's omniverse platform isaac sim combines immersive physically accurate photorealistic environments with complex virtual robots let's look at three very different ai based robots being developed by our partners using isaac sim frown hoffer iml a technology leader in logistics uses nvidia isaac sim for the virtual development of oblax a highly dynamic indoor outdoor， autonomous mobile robot or amr after importing over fifty four hundred parts from cad and rigging with omniverse physics the virtual robot moves just as definitely in simulation as it does in the real world this not only accelerates virtual development， but also enable scaling to larger scenarios next festo well known for industrial automation uses isaac sim to develop intelligent skills for collaborative robots or cobots requiring acute awareness of their environment human partners and tasks festo uses cortex and isaac sim tool that dramatically simplifies programming cobot skills for perception ai models used in this task were trained using only synthetic data generated by isaac replicator finally， there's animal a robot dog developed by a leading robotics research group from eth swiss smile using n to n gpu accelerated reinforcement learning animal whose feet were replaced with wheels learned to walk over urban terrain within minutes rather than weeks using nvidia's isaac gym training tool the locomotion policy was verified in isaac sim and deployed on a real animal this is a compelling demonstration of simulator training for real world deployment from training perception and policies to hardware in loop isaac sim is the tool to build ai based robots that are born in simulation to work and play in the real world。