Jetbot Sample Applications

This section describes how to integrate the Isaac SDK with Omniverse, NVIDIA’s new high-performance simulation platform, to get a Jetbot to follow a ball in simulation. This section serves as a valuable entry point both into Omniverse and the Python API of Isaac SDK using three Jetbot applications. All sample applications are present in jetbot_jupyter_notebook notebook.

Build and Run Jetbot Jupyter Notebook

In the Isaac SDK repository, run the jetbot_jupyter_notebook Jupyter notebook app:

bob@desktop:~/isaac/sdk$ bazel run apps/jetbot:jetbot_jupyter_notebook

Your web browser should open the Jupyter notebook document. If it does not, search for a link on the console: It looks like http://localhost:8888/notebooks/jetbot_notebook.ipynb. Open that link in your browser.

Remote control Jetbot using Virtual gamepad

This sample demonstrates how to control Jetbot remotely using Omniverse and Jupyter notebook.

  • Jetbot in Omniverse: Follow the documentation Isaac Sim built on NVIDIA Omniverse to start the simulator and open the stage at omni:/Isaac/Samples/Isaac_SDK/Robots/Jetbot_REB.usd. Start the simulation and Robot Engine Bridge.

    In the Jupyter notebook, follow the cells to start the SDK application. Once it is connected to the simulator, you can move Jetbot using the virtual gamepad from site in Omniverse.

Remote control Jetbot using Virtual gamepad

Now we are going to build a training environment in Omniverse.

Building a Training Environment

With the Jetbot model working properly and ability to control it through the Isaac SDK, we can now train the detection model, which allows the robot to identify and subsequently follow a ball. As we look to eventually deploy a trained model and accompanying control logic to a real Jetbot, it is very important for the training scene built in Omniverse to be recreatable in the physical world. The simulation environment built in this section was made to mimic the real world environment we create, so you may choose to design your environment differently. NVIDIA recommends using the edges of a cardboard box or pillows as the boundaries of your environment.

We begin building the scene by adding 5 cube meshes, corresponding to 1 floor and 4 walls, by navigating to Create > Mesh > Sphere in the Menu toolbar. By default, the dimensions of the cube are 100cm, so scale and translate them appropriately using the Details panel to create a box of the desired size. As the silver default mesh color of the walls are difficult to recreate in reality, we create a new material, and adjust the coloring and roughness properties of the new OmniPBR material to resemble paper, applying it to the 5 cube meshes. The resulting scene is shown as follows:

Building a Training Environment in Omniverse

Assets can then be added to introduce obstacles that the detection model; the detection model cannot mistake these assets as spheres. From the Content Manager, several assets representing common household items were dragged and dropped onto the stage. The meshes of the added assets were positioned to not intersect with the floor.

Next, we create representations in simulation of the balls our Jetbot will follow. Sphere meshes were added to the scene and were placed within Xform elements to allow domain randomization to be used. Class labels for object detection were added using Semantic Schema Editor. Lastly, Sphere Lights and the jetbot.usd file were added to the scene.

Building a Training Environment in Omniverse

If the scene shown above were used to generate training data and train a detection model, then the ability of the real Jetbot to perform inference using the trained model would suffer unless the physical environment the Jetbot was deployed in exactly matched the above simulation scene. Recreating the intricate details of the scene in the physical world would be exceedingly difficult. Therefore, it is important to create a detection model with the ability to generalize and apply its training to similar physical environments. To accomplish this, Domain Randomization (DR) components are added to the entities in the scene, creating a more diverse training dataset, and thus improving the robustness of the detection model.

All items shown in the scene were free to move within the confines of the paper box, and to rotate about their Z-axis, using DR Movement and Rotation components, respectively. A Color component was applied to the sphere meshes, allowing the detection model to be trained to detect a ball of any color. Light and movement components were added to the sphere lights, so training data could be captured with a variety of shadows and light intensities. Note that the Jetbot model was allowed to move and rotate, so training data could be captured from many locations and angles. The durations of all domain randomization components were set to 0.3 seconds. Save the scene as jetbot_inference.usd. With the simulation environment in place, data can now be collected, and a detection model trained.

Training the Detection Model

To generate a dataset and train a detection model, refer to the Object Detection with DetectNetv2 pipeline in the Isaac SDK documentation, taking note of the following differences. Rather than using Unity3D to generate training images, use Omniverse. The generate_kitti_dataset.app.json file, located in Isaac SDK under packages/ml/apps/generate_kitti_dataset, was altered to instead generate 50000 training images and 500 test images. Before running the generate_kitti_dataset application, be sure that the camera in the Omniverse viewport is switched to the Jetbot’s first person view, the Robot Engine Bridge application is created, and the simulation is started. Note that you must install TensorRT, CUDA, and CuDNN prior to training the detection model with the Transfer Learning Toolkit (TLT), and be sure to follow all installation instructions.

The text files used with the Transfer Learning Toolkit were modified to only detect “sphere” objects. Additionally, as duplicate images are often created during the dataset generation process, the number of epochs was reduced from 100 to 20. The Object Detection pipeline was followed up until the train model (.etlt file) was exported. You are now able to utilize the trained model in our Isaac application to perform inference.

Running Inference in Simulation

This sample demonstrates how to run inference on an object using an existing trained model, Omniverse, and Jupyter Notebook.

  • Jetbot in Omniverse: Follow the documentation Isaac Sim built on NVIDIA Omniverse to start the simulator and open the stage at omni:/Isaac/Samples/Isaac_SDK/Scenario/jetbot_inference.usd. Start the simulation and Robot Engine Bridge.

    In the Jupyter notebook, follow the cells to start the SDK application. Once it is connected to the simulator, you can check on sight window that inferencing output.

Running Inference in Simulation

Jetbot Autonomously Following Objects in Simulation

This sample demonstrates Autonomoulsy Follow Ball Object using Omniverse and Jupyter notebook.

  • Jetbot in Omniverse: Follow the documentation Isaac Sim built on NVIDIA Omniverse to start the simulator and open the stage at omni:/Isaac/Samples/Isaac_SDK/Scenario/jetbot_follow_me.usd. Start the simulation and Robot Engine Bridge.

    In the Jupyter notebook, follow the cells to start the SDK application. Once it is connected to the simulator, you can move the ball in Omniverse and check on sight window that Jetbot is following the ball.

Jetbot Autonomously Following Objects in Simulation