This repository provides the Genesis environment used to legged robots.

1. Create a new python virtual env with python >= 3.9

2. Install genesis

   • pip install genesis-world

3. Install rsl_rl (PPO implementation)

   • Clone https://github.com/leggedrobotics/rsl_rl
   • cd rsl_rl && git checkout v1.0.2 && pip install -e .

4. Install this genesis version of legged_gym

   • Clone this repository
   • cd legged_gym && pip install -e .

same as legged gym. compared with the original version,you only need to add dof_names,max_joint_velocity,links_to_keep in the config flie.

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/<experiment_name>/<date_time>_<run_name>/model_<iteration>.pt. Where <experiment_name> and <run_name> 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.

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 '<your_env>_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 <your_env>.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!

This code builds upon following open-source code-bases. Please visit the URLs to see the respective LICENSES:

1. https://github.com/leggedrobotics/legged_gym/tree/master
2. https://github.com/aCodeDog/awesome-loco-manipulation
3. https://github.com/aCodeDog/legged-robots-manipulation