From b9201f4e7b78356db9e39be4e2ca2c308ea8fd2d Mon Sep 17 00:00:00 2001 From: Liao Dengting <540614105@qq.com> Date: Sun, 28 Jan 2024 21:59:15 +0800 Subject: [PATCH] Add Readme --- README.md | 325 +++++++++++++--------------------------- README_Go1.md | 274 +++++++++++++++++++++++++++++++++ assets/go2_training.jpg | Bin 0 -> 106701 bytes 3 files changed, 376 insertions(+), 223 deletions(-) create mode 100755 README_Go1.md create mode 100644 assets/go2_training.jpg diff --git a/README.md b/README.md index 01b8690..eb081be 100755 --- a/README.md +++ b/README.md @@ -1,268 +1,147 @@ -# Go1 Sim-to-Real Locomotion Starter Kit +# Sim-to-Real project on Unitree Go2 +## Overview -# Table of contents -1. [Overview](#overview) -2. [System Requirements](#requirements) -3. [Training a Model](#simulation) - 1. [Installation](#installation) - 2. [Environment and Model Configuration](#configuration) - 3. [Training and Logging](#training) - 4. [Analyzing the Policy](#analysis) -4. [Deploying a Model](#realworld) - 1. [Installing the Deployment Utility](#robotconfig) - 2. [Running the Controller](#runcontroller) - 3. [RC Configuration](#rcconfig) - 2. [Deploying a Custom Model](#configuration) - 4. [Deployment and Logging](#deployment) - 5. [Analyzing Real-world Performance](#plotting) -5. [Debugging Common Errors](#debugging) +This repository is forked from [walk-these-ways](https://github.com/Improbable-AI/walk-these-ways), which is a Go1 Sim-to-Real Locomotion Starter Kit. It seems that [walk-these-ways](https://github.com/Improbable-AI/walk-these-ways) can be untilized on Unitree [A1](https://github.com/fan-ziqi/dog_rl_deploy) with simple modifications, since those robots are base on [unitree-legged-sdk](https://github.com/unitreerobotics/unitree_legged_sdk). -## Overview +However, the brand-new architecture [unitree-sdk2 ](https://github.com/unitreerobotics/unitree_sdk2)is not base on UDP anymore, so this project aims to train and deploy walk-these-ways on Unitree Go2 by modifying SDK interfaces. -This repository provides an implementation of the paper: +## Requirements +* pytorch 1.10 with cuda-11.3 +* Isaac Gym +* Nvidia GPU with at least 8GB of VRAM +--- +## Train +Clone this repository: - - - Walk these Ways: Tuning Robot Control for Generalization with Multiplicity of Behavior - -
- Gabriel B. Margolis and Pulkit Agrawal -
- Conference on Robot Learning, 2022 -
- paper / - project page -
- - -
- -If you use this repository in your work, consider citing: - -``` -@article{margolis2022walktheseways, - title={Walk These Ways: Tuning Robot Control for Generalization with Multiplicity of Behavior}, - author={Margolis, Gabriel B and Agrawal, Pulkit}, - journal={Conference on Robot Learning}, - year={2022} -} +``` bash +git clone https://github.com/Teddy-Liao/walk-these-ways-go2.git ``` -
- -This environment builds on the [legged gym environment](https://leggedrobotics.github.io/legged_gym/) by Nikita -Rudin, Robotic Systems Lab, ETH Zurich (Paper: https://arxiv.org/abs/2109.11978) and the Isaac Gym simulator from -NVIDIA (Paper: https://arxiv.org/abs/2108.10470). Training code builds on the -[rsl_rl](https://github.com/leggedrobotics/rsl_rl) repository, also by Nikita -Rudin, Robotic Systems Lab, ETH Zurich. All redistributed code retains its -original [license](LICENSES/legged_gym/LICENSE). - -Our initial release provides the following features: -* Train reinforcement learning policies for the Go1 robot using PPO, IsaacGym, Domain Randomization, and Multiplicity of Behavior (MoB). -* Evaluate a pretrained MoB policy in simulation. -* Deploy learned policies on the Go1 using the `unitree_legged_sdk`. - -## System Requirements - -**Simulated Training and Evaluation**: Isaac Gym requires an NVIDIA GPU. To train in the default configuration, we recommend a GPU with at least 10GB of VRAM. The code can run on a smaller GPU if you decrease the number of parallel environments (`Cfg.env.num_envs`). However, training will be slower with fewer environments. - -**Hardware Deployment**: We provide deployment code for the Unitree Go1 Edu robot. This relatively low-cost, commercially available quadruped can be purchased here: https://shop.unitree.com/. You will need the Edu version of the robot to run and customize your locomotion controller. - -## Training a Model - -### Installation - -#### Install pytorch 1.10 with cuda-11.3: - -```bash -pip3 install torch==1.10.0+cu113 torchvision==0.11.1+cu113 torchaudio==0.10.0+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html -``` - -#### Install Isaac Gym - -1. Download and install Isaac Gym Preview 4 from https://developer.nvidia.com/isaac-gym -2. unzip the file via: - ```bash - tar -xf IsaacGym_Preview_4_Package.tar.gz - ``` - -3. now install the python package - ```bash - cd isaacgym/python && pip install -e . - ``` -4. Verify the installation by try running an example - - ```bash - python examples/1080_balls_of_solitude.py - ``` -5. For troubleshooting check docs `isaacgym/docs/index.html` - -#### Install the `go1_gym` package - -In this repository, run `pip install -e .` - -### Verifying the Installation - -If everything is installed correctly, you should be able to run the test script with: - -```bash -python scripts/test.py -``` - -The script should print `Simulating step {i}`. -The GUI is off by default. To turn it on, set `headless=False` in `test.py`'s main function call. - -### Environment and Model Configuration - - -**CODE STRUCTURE** The main environment for simulating a legged robot is -in [legged_robot.py](go1_gym/envs/base/legged_robot.py). The default configuration parameters including reward -weightings are defined in [legged_robot_config.py::Cfg](go1_gym/envs/base/legged_robot_config.py). - -There are three scripts in the [scripts](scripts/) directory: - -```bash -scripts -├── __init__.py -├── play.py -├── test.py -└── train.py -``` - -You can run the `test.py` script to verify your environment setup. If it runs then you have installed the gym -environments correctly. To train an agent, run `train.py`. To evaluate a pretrained agent, run `play.py`. We provie a -pretrained agent checkpoint in the [./runs/pretrain-v0](runs/gait-conditioned-agility/pretrain-v0) directory. - - - -### Training and Logging - -To train the Go1 controller from [Walk these Ways](https://sites.google.com/view/gait-conditioned-rl/), run: - +Start training: ```bash python scripts/train.py ``` -After initializing the simulator, the script will print out a list of metrics every ten training iterations. - -Training with the default configuration requires about 12GB of GPU memory. If you have less memory available, you can -still train by reducing the number of parallel environments used in simulation (the default is `Cfg.env.num_envs = 4000`). - -To visualize training progress, first start the ml_dash frontend app: -```bash -python -m ml_dash.app -``` -then start the ml_dash backend server by running this command in the parent directory of the `runs` folder: -```bash -python -m ml_dash.server . -``` - -Finally, use a web browser to go to the app IP (defaults to `localhost:3001`) -and create a new profile with the credentials: - -Username: `runs` -API: [server IP] (defaults to `localhost:8081`) -Access Token: [blank] - -Now, clicking on the profile should yield a dashboard interface visualizing the training runs. - -### Analyzing the Policy - -To evaluate the most recently trained model, run: +For convenience, [urdf file path](go1_gym/envs/go1/go1_config.py) is directly swtitched from `go1.urdf` to [`go2.urdf`](https://support.unitree.com/home/zh/developer/rl_example). +Play the model: ```bash python scripts/play.py ``` +![Alt text](assets/go2_training.jpg) -The robot is commanded to run forward at 3m/s for 5 seconds. After completing the simulation, -the script plots the robot's velocity and joint angles. +Go2 pretrained model is provided in [./runs](runs/gait-conditioned-agility/pretrain-go2), you can choose whether to use provide pretrained model by modifying the label line `label = "gait-conditioned-agility/pretrain-go2/train"` to your own trained model. -The GUI is on by default. -If it does not appear, and you're working in docker, make sure you haven't forgotten to run `bash docker/visualize_access.bash`. +### Known Issues +* `flip_visual_attachments` in [go1_config](go1_gym/envs/go1/go1_config.py) should be set to `True`, otherwise errors would occur when visualizing. +* To change configuration parameters of env or the robot, you should modify parameters in [go1_config](go1_gym/envs/go1/go1_config.py), not in [legged_robot_config](go1_gym/envs/base/legged_robot_config.py) +--- +## Deploy +Trained policy is only supported to be deployed through your PC or laptop now, because I am not familiar with Jetson Orin, and hope I can fix it and deploy on Jetson Orin. -## Deploying a Model +### Requirements +#### Install LCM +Since [walk-these-ways](https://github.com/Improbable-AI/walk-these-ways) implement an interface based on Lightweight Communications and Marshalling ([LCM](https://github.com/lcm-proj/lcm)) to pass sensor data, motor commands, and joystick state between their code and the low-level control SDK provided by Unitree, LCM should be installed firstly in your PC or laptlop. -### Safety Recommendations - -Users are advised to follow Unitree's recommendations for safety while using the Go1 in low-level control mode. -- This means hanging up the robot and keeping it away from people and obstacles. -- In practice, the main safety consideration we've found important has been not plug anything into the robot's back (ethernet cable, USB) during the initial calibration or when testing a new policy because it can hurt the robot in case of a fall. -- Our code implements the safety layer from Unitree's `unitree_legged_sdk` with PowerProtect level 9. This will cut off power to the motors if the joint torque is too high (could happen sometimes during fast running) -- This is research code; use at your own risk; we do not take responsibility for any damage. - - -### Installing the Deployment Utility - -The first step is to connect your development machine to the robot using ethernet. You should ping the robot to verify the connection: `ping 192.168.123.15` should return `x packets transmitted, x received, 0% packet loss`. - -Once you have confirmed the robot is connected, run the following command on your computer to transfer files to the robot. The first time you run it, the script will download and transfer the zipped docker image for development on the robot (`deployment_image.tar`). This file is quite large (3.5GB), but it only needs to be downloaded and transferred once. - -``` -cd go1_gym_deploy/scripts && ./send_to_unitree.sh +Clone LCM repository to the path you usually place installed softwares, then install LCM: +```bash +git clone https://github.com/lcm-proj/lcm.git +mkdir build +cd build +cmake .. +make +sudo make install ``` -Next, you will log onto the robot's onboard computer and install the docker environment. To enter the onboard computer, the command is: +#### Build unitree_sdk2 +unitree_sdk2 has been inclued in `go2_gym_deploy/unitree_sdk2_bin/library/unitree_sdk2`, you can also clone from [Unitree Robotics](https://github.com/unitreerobotics/unitree_sdk2) to make sure the sdk is updated version. +```bash +cd go2_gym_deploy ``` -ssh unitree@192.168.123.15 +Delete build file +```bash +rm -r build +``` +Install and build: +```bash +sudo ./install.sh +mkdir build +cd build +cmake .. +make ``` -Now, run the following commands on the robot's onboard computer: +### Build lcm_position_go2 +`go2_gym_deploy/unitree_sdk2_bin/lcm_position_go2.cpp` is the core file of this project, which is similar to `lcm_position.cpp` in [walk-these-ways](https://github.com/Improbable-AI/walk-these-ways), but replace unitree_legged_sdk with unitree_sdk2. -``` -chmod +x installer/install_deployment_code.sh -cd ~/go1_gym/go1_gym_deploy/scripts -sudo ../installer/install_deployment_code.sh +Build lcm_position_go2 and generate runfile `lcm_position_go2` +```bash +cd go2_gym_deploy +rm -r build +mkdir build +cd build +cmake .. +make -j ``` -The installer will automatically unzip and install the docker image containing the deployment environment. +All LCM messages in `go2_gym_deploy/lcm_types` are set as the same format shown in [walk-these-ways](https://github.com/Improbable-AI/walk-these-ways) to ensure successful connection with python files. - -### Running the Controller - -Place the robot into damping mode. The control sequence is: [L2+A], [L2+B], [L1+L2+START]. After this, the robot should sit on the ground and the joints should move freely. - -Now, ssh to `unitree@192.168.123.15` and run the following two commands to start the controller. This will operate the robot in low-level control mode. Make sure your Go1 is hung up. - -First: -``` -cd ~/go1_gym/go1_gym_deploy/autostart -./start_unitree_sdk.sh +`xxx_lcmt.hpp` files are generated by: +```bash +lcm-gen -x xxx.lcm ``` -Second: -``` -cd ~/go1_gym/go1_gym_deploy/docker -sudo make autostart +### Verify connection +Connect your PC/Laptop with Go2 robot with ethernet cable and check connection by: +```bash +ping 192.168.123.161 ``` -The robot will wait for you to press [R2], then calibrate, then wait for a second press of [R2] before running the control loop. +Check the network interface address, and copy the network interface address. +```bash +ifconfig +``` +If error occurs, please check [Unitree Support](https://support.unitree.com/home/zh/developer/Quick_start) for details. -### The RC Mapping -![RC Mapping](media/rc_map.png?raw=true) -The RC mapping is depicted above. -### Deploying a Custom Model +### Test communication between LCM and unitree_sdk2 +```bash +cd go2_gym_deploy/build +sudo ./lcm_position_go2 enx10086 +``` +Aeplace `enx10086` with your own network interface address. According to the messages shown in terminal, press `Enter` for several times and the communication between LCM and unitree_sdk2 will set up. -After training a custom model, it will be saved in the `runs` folder (https://github.com/Improbable-AI/walk-these-ways/tree/master/runs/). Note the relative location of your custom model of the `train` folder (for the default policy), it's `gait-conditioned-agility/pretrain-v0/train`. We'll denote this as `$PDIR`. +This command will automatically shut down Unitree sport_mode Service and set the robot to LOW-LEVEL. Please make sure This will Go2 is hung up or lie on the ground. -To play the custom model in simulation first, replace the line https://github.com/Improbable-AI/walk-these-ways/blob/master/scripts/play.py#L97 with `label = "$PDIR"`. +You can verify LCM send by opening a new terminal: +```bash +cd go2_gym_deploy/build +sudo ./lcm_receive +``` -To deploy on the robot, replace the line https://github.com/Improbable-AI/walk-these-ways/blob/master/go1_gym_deploy/scripts/deploy_policy.py#L73 with `label = "$PDIR"`. Then re-run the `send_to_unitree.sh` script to update the files on the robot. +### Load and run policy +Open a new terminate and run: +```bash +cd go2_gym_deploy/scripts +python deploy_policy.py +``` -### Logging and Debugging -Coming soon -### Analyzing Real-world Performance -Coming soon +According to the hints shown in terminal, Press [R2] to start the controller. You can check RC mapping from [walk-these-ways](https://github.com/Improbable-AI/walk-these-ways) page. +**Caution**: +* Press [L2+B] if any unexpected situation occurs!!! +* This is research code; use at your own risk; we do not take responsibility for any damage. -## Debugging Common Errors +Test Video on Unitree Go2: https://www.bilibili.com/video/BV1tQ4y1c7ZG/?spm_id_from=333.999.0.0&vd_source=07873ebe2a113dac57775e264a210929 -| Bug | Solution | First report | -| ----------- | ----------- | ---------- | -| Out of disk space | If you run out of disk space during `cd ~/go1_gym/go1_gym_deploy/installer && ./install_deployment_code.sh` consider changing the script to use `192.168.123.13` instead (at least in my Go1 Edu with 3 Jetson nano, I only had the required disk space to copy the tar and extract the image in only `192.168.123.13`). Alternatively, consider deploying on an external PC. | https://github.com/Improbable-AI/walk-these-ways/issues/7 | -| `lcm_position` syntax error | When deploying with `sudo ./start_unitree_sdk.sh` on an external PC/NUC, if you get the following error: `./lcm_position: 1: Syntax error: word unexpected (expecting ")")`, It is likely because the ./lcm_position has been compiled for ARM aarch64 (to run on the jetson), please recompile it for your architecture(external PC/ NUC) using https://github.com/Improbable-AI/unitree_legged_sdk. | https://github.com/Improbable-AI/walk-these-ways/issues/7 | +## Acknowledgements +* Many thanks to [XiaoxiaoMeitou](https://github.com/Chicken-wings-programing), who provide Nvidia 3060ti and supporting. +* Many thanks to Jony for his support and encourage me to learn basic kownledge about RL. +* Many thanks to xxx, who provide Go2 pretrained model. +## TO DO +* Deploy on Jeston Orin Nano diff --git a/README_Go1.md b/README_Go1.md new file mode 100755 index 0000000..f4e1b1e --- /dev/null +++ b/README_Go1.md @@ -0,0 +1,274 @@ +# Go1 Sim-to-Real Locomotion Starter Kit + +# Table of contents +- [Go1 Sim-to-Real Locomotion Starter Kit](#go1-sim-to-real-locomotion-starter-kit) +- [Table of contents](#table-of-contents) + - [Overview ](#overview-) + - [System Requirements ](#system-requirements-) + - [Training a Model ](#training-a-model-) + - [Installation ](#installation-) + - [Install pytorch 1.10 with cuda-11.3:](#install-pytorch-110-with-cuda-113) + - [Install Isaac Gym](#install-isaac-gym) + - [Install the `go1_gym` package](#install-the-go1_gym-package) + - [Verifying the Installation](#verifying-the-installation) + - [Environment and Model Configuration ](#environment-and-model-configuration-) + - [Training and Logging ](#training-and-logging-) + - [Analyzing the Policy ](#analyzing-the-policy-) + - [Deploying a Model ](#deploying-a-model--) + - [Safety Recommendations ](#safety-recommendations--) + - [Installing the Deployment Utility ](#installing-the-deployment-utility--) + - [Running the Controller ](#running-the-controller--) + - [The RC Mapping ](#the-rc-mapping--) + - [Deploying a Custom Model ](#deploying-a-custom-model--) + - [Logging and Debugging ](#logging-and-debugging-) + - [Analyzing Real-world Performance ](#analyzing-real-world-performance-) + - [Debugging Common Errors ](#debugging-common-errors--) + +## Overview + +This repository provides an implementation of the paper: + + + + + Walk these Ways: Tuning Robot Control for Generalization with Multiplicity of Behavior + +
+ Gabriel B. Margolis and Pulkit Agrawal +
+ Conference on Robot Learning, 2022 +
+ paper / + project page +
+ + +
+ +If you use this repository in your work, consider citing: + +``` +@article{margolis2022walktheseways, + title={Walk These Ways: Tuning Robot Control for Generalization with Multiplicity of Behavior}, + author={Margolis, Gabriel B and Agrawal, Pulkit}, + journal={Conference on Robot Learning}, + year={2022} +} +``` + +
+ +This environment builds on the [legged gym environment](https://leggedrobotics.github.io/legged_gym/) by Nikita +Rudin, Robotic Systems Lab, ETH Zurich (Paper: https://arxiv.org/abs/2109.11978) and the Isaac Gym simulator from +NVIDIA (Paper: https://arxiv.org/abs/2108.10470). Training code builds on the +[rsl_rl](https://github.com/leggedrobotics/rsl_rl) repository, also by Nikita +Rudin, Robotic Systems Lab, ETH Zurich. All redistributed code retains its +original [license](LICENSES/legged_gym/LICENSE). + +Our initial release provides the following features: +* Train reinforcement learning policies for the Go1 robot using PPO, IsaacGym, Domain Randomization, and Multiplicity of Behavior (MoB). +* Evaluate a pretrained MoB policy in simulation. +* Deploy learned policies on the Go1 using the `unitree_legged_sdk`. + +## System Requirements + +**Simulated Training and Evaluation**: Isaac Gym requires an NVIDIA GPU. To train in the default configuration, we recommend a GPU with at least 10GB of VRAM. The code can run on a smaller GPU if you decrease the number of parallel environments (`Cfg.env.num_envs`). However, training will be slower with fewer environments. + +**Hardware Deployment**: We provide deployment code for the Unitree Go1 Edu robot. This relatively low-cost, commercially available quadruped can be purchased here: https://shop.unitree.com/. You will need the Edu version of the robot to run and customize your locomotion controller. + +## Training a Model + +### Installation + +#### Install pytorch 1.10 with cuda-11.3: + +```bash +pip3 install torch==1.10.0+cu113 torchvision==0.11.1+cu113 torchaudio==0.10.0+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html +``` + +#### Install Isaac Gym + +1. Download and install Isaac Gym Preview 4 from https://developer.nvidia.com/isaac-gym +2. unzip the file via: + ```bash + tar -xf IsaacGym_Preview_4_Package.tar.gz + ``` + +3. now install the python package + ```bash + cd isaacgym/python && pip install -e . + ``` +4. Verify the installation by try running an example + + ```bash + python examples/1080_balls_of_solitude.py + ``` +5. For troubleshooting check docs `isaacgym/docs/index.html` + +#### Install the `go1_gym` package + +In this repository, run `pip install -e .` + +### Verifying the Installation + +If everything is installed correctly, you should be able to run the test script with: + +```bash +python scripts/test.py +``` + +The script should print `Simulating step {i}`. +The GUI is off by default. To turn it on, set `headless=False` in `test.py`'s main function call. + +### Environment and Model Configuration + + +**CODE STRUCTURE** The main environment for simulating a legged robot is +in [legged_robot.py](go1_gym/envs/base/legged_robot.py). The default configuration parameters including reward +weightings are defined in [legged_robot_config.py::Cfg](go1_gym/envs/base/legged_robot_config.py). + +There are three scripts in the [scripts](scripts/) directory: + +```bash +scripts +├── __init__.py +├── play.py +├── test.py +└── train.py +``` + +You can run the `test.py` script to verify your environment setup. If it runs then you have installed the gym +environments correctly. To train an agent, run `train.py`. To evaluate a pretrained agent, run `play.py`. We provie a +pretrained agent checkpoint in the [./runs/pretrain-v0](runs/gait-conditioned-agility/pretrain-v0) directory. + + + +### Training and Logging + +To train the Go1 controller from [Walk these Ways](https://sites.google.com/view/gait-conditioned-rl/), run: + +```bash +python scripts/train.py +``` + +After initializing the simulator, the script will print out a list of metrics every ten training iterations. + +Training with the default configuration requires about 12GB of GPU memory. If you have less memory available, you can +still train by reducing the number of parallel environments used in simulation (the default is `Cfg.env.num_envs = 4000`). + +To visualize training progress, first start the ml_dash frontend app: +```bash +python -m ml_dash.app +``` +then start the ml_dash backend server by running this command in the parent directory of the `runs` folder: +```bash +python -m ml_dash.server . +``` + +Finally, use a web browser to go to the app IP (defaults to `localhost:3001`) +and create a new profile with the credentials: + +Username: `runs` +API: [server IP] (defaults to `localhost:8081`) +Access Token: [blank] + +Now, clicking on the profile should yield a dashboard interface visualizing the training runs. + +### Analyzing the Policy + +To evaluate the most recently trained model, run: + +```bash +python scripts/play.py +``` + +The robot is commanded to run forward at 3m/s for 5 seconds. After completing the simulation, +the script plots the robot's velocity and joint angles. + +The GUI is on by default. +If it does not appear, and you're working in docker, make sure you haven't forgotten to run `bash docker/visualize_access.bash`. + + + +## Deploying a Model + +### Safety Recommendations + +Users are advised to follow Unitree's recommendations for safety while using the Go1 in low-level control mode. +- This means hanging up the robot and keeping it away from people and obstacles. +- In practice, the main safety consideration we've found important has been not plug anything into the robot's back (ethernet cable, USB) during the initial calibration or when testing a new policy because it can hurt the robot in case of a fall. +- Our code implements the safety layer from Unitree's `unitree_legged_sdk` with PowerProtect level 9. This will cut off power to the motors if the joint torque is too high (could happen sometimes during fast running) +- This is research code; use at your own risk; we do not take responsibility for any damage. + + +### Installing the Deployment Utility + +The first step is to connect your development machine to the robot using ethernet. You should ping the robot to verify the connection: `ping 192.168.123.15` should return `x packets transmitted, x received, 0% packet loss`. + +Once you have confirmed the robot is connected, run the following command on your computer to transfer files to the robot. The first time you run it, the script will download and transfer the zipped docker image for development on the robot (`deployment_image.tar`). This file is quite large (3.5GB), but it only needs to be downloaded and transferred once. + +``` +cd go1_gym_deploy/scripts && ./send_to_unitree.sh +``` + +Next, you will log onto the robot's onboard computer and install the docker environment. To enter the onboard computer, the command is: + +``` +ssh unitree@192.168.123.15 +``` + +Now, run the following commands on the robot's onboard computer: + +``` +chmod +x installer/install_deployment_code.sh +cd ~/go1_gym/go1_gym_deploy/scripts +sudo ../installer/install_deployment_code.sh +``` + +The installer will automatically unzip and install the docker image containing the deployment environment. + + +### Running the Controller + +Place the robot into damping mode. The control sequence is: [L2+A], [L2+B], [L1+L2+START]. After this, the robot should sit on the ground and the joints should move freely. + +Now, ssh to `unitree@192.168.123.15` and run the following two commands to start the controller. This will operate the robot in low-level control mode. Make sure your Go1 is hung up. + +First: +``` +cd ~/go1_gym/go1_gym_deploy/autostart +./start_unitree_sdk.sh +``` + +Second: +``` +cd ~/go1_gym/go1_gym_deploy/docker +sudo make autostart +``` + +The robot will wait for you to press [R2], then calibrate, then wait for a second press of [R2] before running the control loop. + +### The RC Mapping +![RC Mapping](media/rc_map.png?raw=true) +The RC mapping is depicted above. +### Deploying a Custom Model + +After training a custom model, it will be saved in the `runs` folder (https://github.com/Improbable-AI/walk-these-ways/tree/master/runs/). Note the relative location of your custom model of the `train` folder (for the default policy), it's `gait-conditioned-agility/pretrain-v0/train`. We'll denote this as `$PDIR`. + +To play the custom model in simulation first, replace the line https://github.com/Improbable-AI/walk-these-ways/blob/master/scripts/play.py#L97 with `label = "$PDIR"`. + +To deploy on the robot, replace the line https://github.com/Improbable-AI/walk-these-ways/blob/master/go1_gym_deploy/scripts/deploy_policy.py#L73 with `label = "$PDIR"`. Then re-run the `send_to_unitree.sh` script to update the files on the robot. + +### Logging and Debugging +Coming soon +### Analyzing Real-world Performance +Coming soon + + +## Debugging Common Errors + +| Bug | Solution | First report | +| ----------- | ----------- | ---------- | +| Out of disk space | If you run out of disk space during `cd ~/go1_gym/go1_gym_deploy/installer && ./install_deployment_code.sh` consider changing the script to use `192.168.123.13` instead (at least in my Go1 Edu with 3 Jetson nano, I only had the required disk space to copy the tar and extract the image in only `192.168.123.13`). Alternatively, consider deploying on an external PC. | https://github.com/Improbable-AI/walk-these-ways/issues/7 | +| `lcm_position` syntax error | When deploying with `sudo ./start_unitree_sdk.sh` on an external PC/NUC, if you get the following error: `./lcm_position: 1: Syntax error: word unexpected (expecting ")")`, It is likely because the ./lcm_position has been compiled for ARM aarch64 (to run on the jetson), please recompile it for your architecture(external PC/ NUC) using https://github.com/Improbable-AI/unitree_legged_sdk. | https://github.com/Improbable-AI/walk-these-ways/issues/7 | + diff --git a/assets/go2_training.jpg b/assets/go2_training.jpg new file mode 100644 index 0000000000000000000000000000000000000000..a33b09ea8b1dfec1d3148df6f383178a76cf7931 GIT binary patch literal 106701 zcmeFYcUV(RyEnS%2qGdNov0|iNJm;w9zaB-2}q4f?;;>YNK~5AB1%ydf)oJ}LI-J) z8U+FA2+~D5B$N;!+J7b`}?kQ_Idtz1{N7+&8#(Zx4D1!J)w?KXK9%A zLOfgnz}Og&1OR{;V5B(((1A4?@CTsb2N?dW1ArBcz<<;oX~h58hZX>6Ise^1)&rpb zXMgaSf0nEN{Qn=H$DRWK6WBt3Q9(ieIUN9w2e`m%EScsX?KHG>z>Ana>!-l>wd{ZO zm%n&1o95qd%BKC-n~ssP>Hbxxf0M`f&pzPbqoQz8K}r3hyt;zg8HJ1L3aaWCm4M3@ zx`u|}JqxG%E-tfvCW>pnOsunrOy;Bxy%J@2S~ z_aF7TKl|J?*9H501M6p;-0wJp^);|AefK}LyZxuSk9W|~dymS|?2o(nT3UdgN1PG_ z{s26H0N@_r4cq}jfir+9p!mP&uk>esUBD0Q9SYnB$GHLSKp;5gGT3tT1Q)Pw2>1d{ zfGi*l))jz@;H7XxXK*Z72*dxRpY*@mBBudB3qz$+8~)vP4Fdq>ssO<9>ECVQY5>5& z3IJa|-Me%D&YzDvl5AR6P&T&;0D#Q~064#cW9$N46&3!xPjmbV05D8ZsoSEU;JpL@ z@^dQnFo#Md=K=uTPXPGrOI-(Un}Raq2MW%;Gm5eo0ktcJ#`H&G0svxyG`0Ypzy$-r zBh3J41!*+_MoB;LihWwVT}jJ=+qb^DmM19bw3 z0tN7>(1O2ow6wHz^mJgMXP`eS42%qa7RG-TrauewQ91S><102kBYK+cwH0J9*&n19=z;* zrJEj>LQJ=4*8XlR3v7Ry1; za8{o2)MYcKJN}&K6dp5kT}ggd-f~Pt(HzHp_kKUi>GMjn7w|_*`g2A9y#+n_KU&d$ zEa*ShLmdZ>)6swoqvHUe02P29i03?F`TP1y(f~^R{x^sIlAQUM#z3k4e{<+B&Y^+7 zIrJCj(BR)3`ipaD=x+}F#W^(mH;4Y>92)tXLw|7&jsDG{zc`2f#zTK?wEj09`WG_% ztA0YMf8(LQx(@w~hyLmu`Wp}Z)j9M(i-(HkZ6OE+*q%HfKCw?2Wqq zS#OjkUQ&Uo)$dgpqS)qoONrj12kIEf;+%gU!KF0`UMO*>I%aL~h0A(L^feWrFQNiZ z;ymjPRa+q^3!&R146#%|Utu)YeHy2SBuc!mrWDTx_0(bQ;@7q1`b#g9CCO2T4E_6F zdhjj}DDEUG+OA648~zw_@N^;Iz`DR4gY!j`xOmKi_!F{t2UB(xJ!Qv8 z=W4^yABNU=3})83$=n_=oR9VT%Rp7nW?5WnK}ApKS+a}{+3UN~CQ-#?ekTv%NxJs^ z3}mNk{I|(sMe$0I217ros~t84Xc-S*v|52|AEQ%E6Nd+oB<1-=ciQME-4=RB3O{u8Khnuf%CXl4 zXHAIKG2wgZ=0gHhz?UJ+6=WKHJUG~Q=`*WIRK4Ol70CKMhad|`S;b4D0tJ_-fSS4# zSwRU!6jNQ}e3#%c&D40Vsp7!0Dt0?iv`IvtoD6k=u%CPX4AE&tMH55_YwQx=Y7R^6%o^HFfP@W3V-if0E zhU$_d7ANKwp}lP;AjRYmErz>7IdU=SW{Z@tpW|36@a~!v2HXi}BBycY-Q;(Td>(!c zl(3aFU5NcPFEYJ&$!W9emwu4=moYd$9un7$Lx@KWZaodLSX1;CF;QD;2XtKh9%cf=K~nX}YW_ntz=?8j zwRjNJJ{8suea%%U&pHSBY=7n>lI=Po72$BxQn~_p`?uRxbv~k{EQ{^BP{7>oug2p~ zm7dNoj#YyKExBVXM`24PXUi6>z5lSSRko7Cqa~12AX%ZzQ`x z=*%NfjBglgMH*+X1mC(u-pEzaM#)O`2Tl{usggMDZmio$(o0Hh4#lI z6pZ0*W(LEc-iX_iuGE8s4F_41Su4<3qG}TxY+Rr4HbSAg1BPh%G+h?)1?t>{=dKC! zY1GG(cRA40l9KRzqx~z3+vq~@rPb5r3tdhoc(-RINc3xvL+>70a~C06DnJ!|O9i%A zi1Bj*aA=Z%%_S<(d>WHKF6js}D6^;72C)-W1ZIa4slX{zd{}}AC1cslkP2k?rnZBx zE@NMWto0>0b+7+ks)OeLmOALkDL7O?-q}_pZVy9e#t?D1G=8~`MA7gQn(Hbdp;X{e z&xO#D0?-ci!iUTB?F{jY(Xz}GKJ5vfty+KBcp^9SM@j>eA~aTm3J_JM2`jb6Bm8@j z8xaN*zJvQeMj60qH^OjcQl}@pd}b%6LxL_cQS{=}DO>LO&`LlVeQC3_bolDBN{+qBvWEV-U6#angqhxQ^8>u_5x6xgkGJ zE@68SuP;!6HHJg4W#^K;*6CBC&=rV0<(gN_5Y)MEJks!XJ+iHjqb=+8pe%woi73oJ zYpJF0DQwyhdx>(3hYHX%t9dvl8no2^5(wKI+^~kc4Z==^KsxIYJO^59yr6AM+mhQh zgRP9*uJOa1f_#W85Jl!)3%HQSw8~Efq74WJulD=4q9U{@()(ps8^RW@VwZ2OlI`B( zAfGesC|9PAeV4_o*1Q49aKPnSa459L?v6-f;HG5r z7p&PG3a`SpxQUjtd<~>=nIwcfo4-1m#92UK8%ZgBheNcAi9keOdhub0IgI7jLW55P zkb2zFJCQ?}nHMPB4K{0vHk@PW85OYq1B@js6`igVKoid)NSq2QR_PS(xAGfn=?709 z??BuOL-lk(>$H+!eZc4m!CgiZMN>BqP)v^FRA5xbYL5!Ir4~QKfST|SGn1KqJN}_i z*r=9flkrnfQQYHsrDNs#T8FyMUn80n8uCzyV1jo(7t+$Kn=vFX?~ z!$EBcPiBo{-sk+t3>{)=!)t;|6diH&s_-F&?OEMErcztFkh1wEAJqR!a2P25eb1gi ziRmQELB6K*gZXC2RtwXugQnPT$c2hHO4=?sAveN{D99S}X_i3U&i0*#_@qgW)o74* z;F>3HOhA56Byfqnh~+uF{QkK4K_YD7gfl{8$a?ce5vW2-;Cyw~-o^_cSsl?%!nx-t zFYIJOf4>8b2|XyqDL+o1rtlAxw(e{NdC8*+UM-U);RP0D0l9eBII>$K*hLu8a>KjL zX%?GVo1j;;TmUuMNw#MM)sr4^^nm3vZ_rWWxuB$HDcBnChgv9`41Yj~ZyZUlkrqluH?NyO1#+!q0&K@;e-=Lw;QlwO0HBdhu0JplCfq z|JDWW9d1xf-W{4OhkHWV!}U%;o8H?a$p!WieJyecHF{~AtzqqY>M`YSov46d;XQX* zq)bi&D-sCV7^G-d*%2{QHOYvK5y1nMO3*$+tX^P|oB9UZT%T@ztCzG+x(c*lP;Ptus|bT;EYH%L|xOl!1XCtXM1WB;MNbMCmk7}J*eW{}C!R;6$7T9I zbum%@@3HAzF2GFh=o05!)_ZhKaVSp+0bSn`Ivz8=qnfobDVJ7p3P|OmO%JTGgxJYHR&Xj!U8Sw*=|%Wu%!cKznYg3%Fq*B z??w`60Y}p}{oasa917gu1rR%2%CMV7q<4XEPBYl|jZ{#EC*VK!-mYk$!wsWGLZ_*~ z<(LLC=uf^P=edP@qE0qy_%;`D4^pyh2X9GYx@4UZN`cn9s-TXwlwnRTK=A6s%7V+F zby51{LIu7TBsGa0IU%r}iz`9M1jSVBIU(-LeI%AOysdOI`u3v1mZ~1YB}0`Y0KuI) z1+ArshCKNde5h6O5t6p4(xeMoj7TrU-T0dco^Ww|bU&;q{*Cw^+{%sHOS-r^x)(Wj zB94Nq>LaSUQI0J-f1X5gPB~E?db%r80eHzgS|4=Go*;0>4wqC^qx?2PdtHWQzaE5_ z=5ix`Sc3lS;-(%V=LSes`kSDFc`ny&HS@QLLKjOWf1wVzI-6LH!DbUgi^fj=4uuvAq)7=Kv1o3}YxUqG8_YX!g&a9+5;N#WR_mPp#=oN_M}U%3 zt6=*KBsJVGHQssSnq?{wZuv3G;VYqFKRmzW8){|VX@{}Ohops`tVGX^a(GW20?k%30{nlziVC;j&g9P(qMG$ry29aOJX)v^wDJy8PbeAAK&IPJ$) zL%UD4&BEH(&1yWF1&3wX^Q;1(lqVny

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