https://github.com/leggedrobotics/legged_gym

Isaac Gym Environments for Legged Robots
https://github.com/leggedrobotics/legged_gym

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Isaac Gym Environments for Legged Robots

• Host: GitHub
• URL: https://github.com/leggedrobotics/legged_gym
• Owner: leggedrobotics
• License: other
• Created: 2021-10-18T12:50:04.000Z (about 3 years ago)
• Default Branch: master
• Last Pushed: 2024-08-02T10:52:49.000Z (5 months ago)
• Last Synced: 2024-10-29T15:38:50.925Z (2 months ago)
• Language: Python
• Size: 35.3 MB
• Stars: 1,333
• Watchers: 39
• Forks: 376
• Open Issues: 45
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

• awesome-legged-locomotion-learning - [**legged_gym**
• awesome-isaac-gym - Code

README

        
# Isaac Gym Environments for Legged Robots #

This repository provides the environment used to train ANYmal (and other robots) to walk on rough terrain using NVIDIA's Isaac Gym.

It includes all components needed for sim-to-real transfer: actuator network, friction & mass randomization, noisy observations and random pushes during training.  

**Maintainer**: Nikita Rudin  

**Affiliation**: Robotic Systems Lab, ETH Zurich  

**Contact**: [email protected]  

---

### :bell: Announcement (09.01.2024) ###

With the shift from Isaac Gym to Isaac Sim at NVIDIA, we have migrated all the environments from this work to [Isaac Lab](https://github.com/isaac-sim/IsaacLab). Following this migration, this repository will receive limited updates and support. We encourage all users to migrate to the new framework for their applications.

Information about this work's locomotion-related tasks in Isaac Lab is available [here](https://isaac-sim.github.io/IsaacLab/source/features/environments.html#locomotion).

---

### Useful Links ###

Project website: https://leggedrobotics.github.io/legged_gym/   

Paper: https://arxiv.org/abs/2109.11978

### Installation ###

1. Create a new python virtual env with python 3.6, 3.7 or 3.8 (3.8 recommended)

2. Install pytorch 1.10 with cuda-11.3:

    - `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`

3. Install Isaac Gym

   - Download and install Isaac Gym Preview 3 (Preview 2 will not work!) from https://developer.nvidia.com/isaac-gym

   - `cd isaacgym/python && pip install -e .`

   - Try running an example `cd examples && python 1080_balls_of_solitude.py`

   - For troubleshooting check docs `isaacgym/docs/index.html`)

4. Install rsl_rl (PPO implementation)

   - Clone https://github.com/leggedrobotics/rsl_rl

   -  `cd rsl_rl && git checkout v1.0.2 && pip install -e .` 

5. Install legged_gym

    - Clone this repository

   - `cd legged_gym && pip install -e .`

### CODE STRUCTURE ###

1. Each environment is defined by an env file (`legged_robot.py`) and a config file (`legged_robot_config.py`). The config file contains two classes: one containing  all the environment parameters (`LeggedRobotCfg`) and one for the training parameters (`LeggedRobotCfgPPo`).  

2. Both env and config classes use inheritance.  

3. Each non-zero reward scale specified in `cfg` will add a function with a corresponding name to the list of elements which will be summed to get the total reward.  

4. Tasks must be registered using `task_registry.register(name, EnvClass, EnvConfig, TrainConfig)`. This is done in `envs/__init__.py`, but can also be done from outside of this repository.  

### Usage ###

1. Train:  

  ```python legged_gym/scripts/train.py --task=anymal_c_flat```

    -  To run on CPU add following arguments: `--sim_device=cpu`, `--rl_device=cpu` (sim on CPU and rl on GPU is possible).

    -  To run headless (no rendering) add `--headless`.

    - **Important**: To improve performance, once the training starts press `v` to stop the rendering. You can then enable it later to check the progress.

    - The trained policy is saved in `issacgym_anymal/logs//_/model_.pt`. Where `` and `` are defined in the train config.

    -  The following command line arguments override the values set in the config files:

     - --task TASK: Task name.

     - --resume:   Resume training from a checkpoint

     - --experiment_name EXPERIMENT_NAME: Name of the experiment to run or load.

     - --run_name RUN_NAME:  Name of the run.

     - --load_run LOAD_RUN:   Name of the run to load when resume=True. If -1: will load the last run.

     - --checkpoint CHECKPOINT:  Saved model checkpoint number. If -1: will load the last checkpoint.

     - --num_envs NUM_ENVS:  Number of environments to create.

     - --seed SEED:  Random seed.

     - --max_iterations MAX_ITERATIONS:  Maximum number of training iterations.

2. Play a trained policy:  

```python legged_gym/scripts/play.py --task=anymal_c_flat```

    - By default, the loaded policy is the last model of the last run of the experiment folder.

    - Other runs/model iteration can be selected by setting `load_run` and `checkpoint` in the train config.

### Adding a new environment ###

The base environment `legged_robot` implements a rough terrain locomotion task. The corresponding cfg does not specify a robot asset (URDF/ MJCF) and has no reward scales. 

1. Add a new folder to `envs/` with `'_config.py`, which inherit from an existing environment cfgs  

2. If adding a new robot:

    - Add the corresponding assets to `resources/`.

    - In `cfg` set the asset path, define body names, default_joint_positions and PD gains. Specify the desired `train_cfg` and the name of the environment (python class).

    - In `train_cfg` set `experiment_name` and `run_name`

3. (If needed) implement your environment in .py, inherit from an existing environment, overwrite the desired functions and/or add your reward functions.

4. Register your env in `isaacgym_anymal/envs/__init__.py`.

5. Modify/Tune other parameters in your `cfg`, `cfg_train` as needed. To remove a reward set its scale to zero. Do not modify parameters of other envs!

### Troubleshooting ###

1. If you get the following error: `ImportError: libpython3.8m.so.1.0: cannot open shared object file: No such file or directory`, do: `sudo apt install libpython3.8`. It is also possible that you need to do `export LD_LIBRARY_PATH=/path/to/libpython/directory` / `export LD_LIBRARY_PATH=/path/to/conda/envs/your_env/lib`(for conda user. Replace /path/to/ to the corresponding path.).

### Known Issues ###

1. The contact forces reported by `net_contact_force_tensor` are unreliable when simulating on GPU with a triangle mesh terrain. A workaround is to use force sensors, but the force are propagated through the sensors of consecutive bodies resulting in an undesirable behaviour. However, for a legged robot it is possible to add sensors to the feet/end effector only and get the expected results. When using the force sensors make sure to exclude gravity from the reported forces with `sensor_options.enable_forward_dynamics_forces`. Example:

```

    sensor_pose = gymapi.Transform()

    for name in feet_names:

        sensor_options = gymapi.ForceSensorProperties()

        sensor_options.enable_forward_dynamics_forces = False # for example gravity

        sensor_options.enable_constraint_solver_forces = True # for example contacts

        sensor_options.use_world_frame = True # report forces in world frame (easier to get vertical components)

        index = self.gym.find_asset_rigid_body_index(robot_asset, name)

        self.gym.create_asset_force_sensor(robot_asset, index, sensor_pose, sensor_options)

    (...)

    sensor_tensor = self.gym.acquire_force_sensor_tensor(self.sim)

    self.gym.refresh_force_sensor_tensor(self.sim)

    force_sensor_readings = gymtorch.wrap_tensor(sensor_tensor)

    self.sensor_forces = force_sensor_readings.view(self.num_envs, 4, 6)[..., :3]

    (...)

    self.gym.refresh_force_sensor_tensor(self.sim)

    contact = self.sensor_forces[:, :, 2] > 1.

```