When you shipping or sending a package, you will get RL Carriers shipping receipt. This process is known as domain randomization and it is a common technique in transfer learning.How to find tracking number from RL Carriers Malta Europe branch? You also spawn random meshes, known as distractors, to cast hard shadows on the track and help teach the network what to ignore. ![]() You do this by periodically randomizing the track, lighting, and so on. You can’t simulate every possibility, so instead you teach the network to ignore variation in these things. Differences in lighting, colors, shadows, and so on means that the domain your network encounters after being transferred to the real JetBot is quite large. We also wanted to create an agent that didn’t require a specific setup to function. If you see the reward plateauing after a few hundred thousand updates, you can reduce the learning rate to help the network continue learning. You should see the network start to display consistent turning behavior after about 100k updates or so. To shorten this, convert all images from RGB to grayscale. However, we found that it took several hundred thousand updates to the network for it to start driving consistently. We originally trained using the full RGB output from the simulated camera. Also changing the dashed lines color from yellow to white. Matching real and simulation camera parameters. Python3 -m pip install stable_baselines3=0.8.0įigure 13. Install stable-baselines by pressing the plus (+) key in the Jupyter notebook to launch a terminal window and run the following two commands: apt install python3-scipy python3-pandas python3-matplotlib Unplug the keyboard, mouse, and HDMI to set your JetBot free. If the setup succeeded without error, the IP address of the JetBot should be displayed on the LED on the back of the robot. enable.sh $HOME, you can connect to the JetBot from your computer through a Jupyter notebook by navigating to the JetBot IP address on your browser, for example. Running the following two commands from the Jupyter terminal window also allows you to connect to the JetBot using SSH: apt install openssh-server If you see docker: invalid reference format, set your environment variables again by calling source configure.sh. These must be run on the JetBot directly or through SSH, not from the Jupyter terminal window. Launch Docker with all the steps from the NVIDIA-AI-IOT/jetbot GitHub repo, then run the following commands. If you are using the 2GB Jetson Nano, you also need to run the following command: $ sudo apt-get dist-upgradeĪfter setting up the physical JetBot, clone the following JetBot fork: $ git clone Update the package list: $ sudo apt-get update Boot up and follow the onscreen instructions to set up the JetBot user. Plug in a keyboard, mouse, and HDMI cable to the board with the 12.6V adapter. Put the microSD card in the Jetson Nano board. Also, the 2GB Jetson Nano may not come with a fan connector.įlash your JetBot with the following instructions: On the Waveshare Jetbot, removing the front fourth wheel may help it get stuck less. The 4GB Jetson Nano doesn’t need this since it has a built in Wi-Fi chip.įor more information about supported components, see Networking.Īssemble the JetBot according to the instructions. ![]()
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